A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis

Malika Bendechache; Mousa Ahmad Al-Bashrawi; Muhammad Owais; Muhammad Zubair; Muzammil Hussai

REVIEW 3 major objections 4 minor 1 cited by

A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This review claims that fusing structural and functional medical scans into one image improves diagnostic accuracy, and maps the techniques, applications, and obstacles behind that claim.

desk verdict Useful organizational survey of MMIF, but with wrong equations and clinical claims that outrun the evidence. read the letter →

arxiv 2505.14715 v1 pith:TJFPGEGC submitted 2025-05-18 eess.IV cs.CV

Muhammad Zubair , Muzammil Hussai , Mousa Ahmad Al-Bashrawi , Malika Bendechache , Muhammad Owais This is my paper · ORCID

classification eess.IVcs.CV

keywords multi-modalmedicalimagefusionimagingmodalitiesalgorithmsdeeplearningtransformer-basedcomputer-aideddiagnosisclinicaldecisionsupportdiagnosticaccuracy

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a systematic survey arguing that multi-modal medical image fusion (MMIF), which combines structural scans like CT and MRI with functional scans like PET and SPECT into a single image, improves diagnostic accuracy, lesion detection, and treatment planning. Its contribution is reference value: a structured map of the fusion levels, the algorithm families used at each level, the modality combinations applied to specific organs, and the clinical tasks each combination serves. The authors take the field's progress as real, from hand-crafted pixel-level rules to deep learning and transformer architectures, but they stress that routine clinical adoption still awaits explainable AI, privacy-preserving training, real-time systems, and standardization.

What carries the argument

The load-bearing structure is the review's taxonomy: a three-level hierarchy of fusion abstraction, pixel-level, feature-level, and decision-level, with hybrid pixel-feature methods in between, cross-referenced against six algorithm families (morphological operations, human-visual-system operators, neural networks, sub-band decomposition, fuzzy logic, and hybrid methods) and against tables mapping modality combinations to organs. These tables carry the argument: they are what allow the review to claim that specific techniques, such as NSST with pulse-coupled neural networks for MRI-PET or guided filtering for CT-MRI, fit specific clinical tasks. The accompanying evaluation metrics, SSIM, PSNR, entropy, spatial frequency, and the Xydeas-Petrovic edge-preservation index, supply the quantitative language in which the surveyed papers report success.

What would settle it

A prospective, multi-center reader study in which blinded clinicians diagnose the same cases from fused images and from the best single modality would settle the central claim: if fused reads do not improve diagnostic accuracy, lesion detection, or inter-reader agreement over the single best modality, the review's synthesis overstates MMIF's clinical readiness. A cheaper partial test is correlational: compute whether the SSIM, PSNR, and entropy gains reported across the surveyed papers predict expert-rated diagnostic utility on the same image pairs.

Watch

Extended reading notes

Core claim

The review's central claim is that no single imaging modality captures the full complexity of disease, and that fusing complementary modalities yields a representation genuinely more informative than any input alone: CT's anatomy plus PET's metabolism, or MRI's soft-tissue contrast plus SPECT's perfusion data. On that basis it asserts that MMIF significantly advances diagnostic accuracy, lesion detection, and segmentation, and it treats PET/CT in oncology as the mature proof of concept. The review's own reading of the literature is that the field has moved through three generations, from hand-crafted pixel- and transform-domain rules, to intelligent optimization and hybrid methods, to deep learning, GAN, and transformer models that learn fusion end to end, with each generation shifting the trade-off between robustness, computational cost, and interpretability. It also concludes that clinical use is still gated by missing standardized datasets, computational demands, privacy regulation, and the absence of rigorous multi-center validation.

Load-bearing premise

The review's upbeat conclusion rests on treating the quantitative fusion metrics reported across surveyed studies (SSIM, PSNR, entropy) as proxies for real clinical benefit; the paper itself concedes in Section 9.5 that multi-center clinical validation is still absent and that errors in fused images can cause misdiagnosis.

Editorial extensions

If this is right

If the review is right, hybrid PET/CT-style fusion will extend to more organ systems and modality pairs, and PET/MRI will keep expanding in neurology and oncology as hardware matures.
Deep learning and transformer-based fusion, which learn cross-modal mappings without hand-crafted rules, will keep displacing classical pixel-level and transform-domain methods as the default research direction.
Clinical adoption will hinge on the bottlenecks the review names: explainable models clinicians can trust, real-time point-of-care systems, federated learning for privacy, and regulatory standardization.
Fusion quality will have to be judged by task-level outcomes such as segmentation accuracy, diagnostic confidence, and decision impact rather than by pixel-level metrics alone, a shift the review explicitly endorses.
The review's negative finding, that standardized multi-modal datasets and multi-center validation are still scarce, implies that reported performance gains in the literature have not yet been converted into generalizable clinical tools.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A reader could take the abstract's confident framing as a research agenda rather than an established clinical verdict: the review's own Section 9.5 concedes that rigorous clinical validation is lacking, so the strongest defensible claim is that MMIF is promising and well mapped, not yet proven at the bedside.
The proxy assumption that SSIM, PSNR, and entropy gains equal diagnostic benefit is directly testable: one could correlate the published fusion-metric improvements with expert radiologist ratings or with measured changes in diagnostic accuracy on the same image pairs.
The taxonomy suggests a prediction the authors do not draw: decision-level fusion, currently the least-used level, should gain ground as imaging is fused with genomic, proteomic, and clinical data, since decision-level methods are the natural fit for heterogeneous non-image inputs.
An editorial test of the survey's organizing claim would be a reader study comparing fused images against the best single modality for the same cases, because the review assembles little direct evidence that fused images change clinician decisions.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Hi colleague,

Quick take on arXiv:2505.14715, the MMIF review by Zubair et al. It's a review, so don't expect new results. What it does well is organize the field: the taxonomy of pixel-, feature-, hybrid-, and decision-level fusion, and the big tables mapping techniques to modalities and clinical applications (Tables 6 and 7) are a genuinely useful entry point for someone new to MMIF. The narrative is broadly consistent with the literature I know, and the challenge sections (9 and 10) hit the right topics: computational cost, data privacy, heterogeneity, interpretability, and the gap between algorithmic development and clinical adoption.

The soft spots are real but fixable. First, the equations. The SSIM formula (Eq. 8) is wrong—it inverts numerator and denominator and isn't bounded by 1. The two Sobel kernels in Eq. 3 are identical, which makes the edge-intensity computation degenerate. Those aren't just typos; they're in the section that's supposed to define the metrics the review leans on. Second, there's no stated methodology for literature selection. It calls itself 'comprehensive' but doesn't say how references were chosen, so its authority as a systematic map is limited. Third, and most important, the clinical claim outruns the evidence. The abstract says MMIF 'significantly advances diagnostic accuracy, lesion detection, and segmentation,' but the evidence cited is mostly image-quality metrics—SSIM, PSNR, entropy. The paper's own Section 9.5 admits that rigorous multi-center clinical validation is lacking and that errors in fused images can cause misdiagnosis. That's an internal tension that needs resolving: either the abstract gets tempered or the evidence section gets strengthened.

Self-citations are present but not load-bearing, so I wouldn't hold that against it.

My recommendation: if this lands on an editor's desk, it merits peer review rather than a desk reject. A competent referee would ask for major revision: correct the equations, add a search strategy, and bring the abstract and conclusion in line with the actually-available evidence. I'd use the paper as a starting point for a reading group discussion on how image-fusion evaluation metrics relate (or don't) to clinical outcomes—that's a valuable conversation. I wouldn't cite it in my own work because I'd rather cite the primary sources.

Cheers.

Referee Report

3 major / 4 minor

Summary. This manuscript is a narrative survey of multi-modal medical image fusion (MMIF), covering the major imaging modalities (X-ray, CT, MRI, ultrasound, fMRI, PET, SPECT), the standard taxonomy of fusion levels (pixel-, feature-, hybrid, and decision-level), a catalogue of fusion algorithms (morphological, HVS-based, neural-network, sub-band, and fuzzy-logic methods), evaluation metrics, clinical applications, challenges, and future directions. The paper claims that MMIF 'significantly advance[s] diagnostic accuracy, lesion detection, and segmentation' and positions the field as poised for routine clinical adoption. The review is organized as a broad reference rather than a focused technical contribution, with Tables 6 and 7 providing a large compilation of recent studies, fusion techniques, modalities, and reported contributions.

Significance. If the survey's technical content were accurate and its clinical claims appropriately calibrated, this would be a useful reference for researchers and clinicians entering the MMIF area. The paper's strengths are its breadth of coverage, its structured presentation of fusion levels and algorithm families, and its explicit enumeration of challenges such as data privacy, standardization, and the need for clinical validation. The authors also deserve credit for acknowledging, in Section 9.5, that rigorous multi-center clinical validation is still lacking. However, the review's positive synthesis is undermined by (i) two concrete technical errors in the definitions of standard metrics that are used as evidence throughout the paper, and (ii) a mismatch between the abstract's strong clinical-readiness claims and the paper's own admission that fusion metrics have not been shown to translate into improved patient outcomes. These issues are fixable, but they currently limit the manuscript's reliability as a reference.

major comments (3)

[Section 7.8, Eq. (8)] The SSIM formula as written is inverted relative to the standard definition. The conventional expression is SSIM(x,y) = [(2μxμy + C1)(2σxy + C2)] / [(μx² + μy² + C1)(σx² + σy² + C2)], giving values bounded by 1, but Eq. (8) has the numerator and denominator swapped. As written, the stated property that 'SSIM values close to 1 indicate high structural similarity' is not guaranteed, and the metric can exceed 1. Since SSIM is a central evaluation metric referenced throughout the survey, this error needs correction.
[Section 7.3, Eq. (3)] The two Sobel kernels displayed for hx and hy are identical, and neither matches the standard Sobel operator: the x-kernel has the wrong sign in the first and third rows, and the y-kernel is missing. The edge intensity measure EI is therefore degenerate, as it cannot distinguish horizontal and vertical gradient information. This undermines the credibility of edge-intensity as an evaluation metric in the survey's technical presentation.
[Abstract and Conclusion vs. Section 9.5] The Abstract and Conclusion state that MMIF 'significantly advance[s] diagnostic accuracy, lesion detection, and segmentation' and that the field is close to 'routine clinical use,' but Section 9.5 concedes that 'rigorous multi-center clinical validation is still lacking' and that errors in fused images 'can carry serious consequences, including misdiagnosis.' The evidence cited in Tables 6–7 consists mainly of image-quality metrics (SSIM, PSNR, entropy, average gradient, spatial frequency) with no established connection to patient-level diagnostic outcomes. The manuscript should either temper its clinical-readiness claims to match the stated evidence or provide a reasoned justification for why these quality metrics are valid proxies for clinical benefit.

minor comments (4)

[Section 4.4.1] The subsection titled 'Decision-learning-based fusion' actually describes dictionary-learning-based fusion (e.g., Gaussian filter with integrated dictionary learning, image patch sampling, sparse representation), which is a feature-level technique, not a decision-level one. This placement is inconsistent with the taxonomy in Section 4 and with Table 4, and it creates confusion about the paper's classification scheme.
[Section 5.2 heading] The heading 'Algorithms using Human Value System operations' should be 'Human Visual System operations' to match the text's own abbreviation HVS.
[Tables 1, 2, and 4] Several typos should be corrected: 'prons' should be 'pros' in Tables 1 and 2, and 'indivisdual sternghts, limistations' should be 'individual strengths, limitations' in Table 4.
[General] There are repeated grammatical and spelling issues throughout the manuscript (e.g., 'In contrary to CT' in Section 2.1.3, 'where RF row frequency and CF (column frequency) are computed' in Section 7.7). A careful proofreading pass is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: survey synthesizes external literature; self-citations are not load-bearing.

full rationale

This paper is a review of multi-modal medical image fusion techniques, not a derivation of new results. Its central claims—for example that MMIF 'significantly advance[s] diagnostic accuracy, lesion detection, and segmentation'—are presented as a synthesis of surveyed external studies and are not derived from the paper's own definitions, equations, or fitted parameters. The evaluation metrics in Section 7 (AG, SD, EI, IE, PSNR, SF, SSIM) are standard external definitions used to describe the surveyed literature; none of these quantities is used to define a target result that is then 'predicted' by construction. The authors cite several of their own prior works (e.g., refs. 13, 16, 19-22, 24), but these citations appear as ordinary examples of CAD systems and related medical-imaging work; the review's comparative taxonomy, modality overviews, and challenge enumeration do not rest on those self-citations. The paper's own Section 9.5 concedes that rigorous multi-center clinical validation is lacking and that errors in fused images can cause misdiagnosis; this is an internal evidence-strength limitation, not a circularity. No step in the paper reduces an output to an input by definition, no fitted parameter is renamed as a prediction, and no unique theorem from the authors' prior work is invoked to force the review's structure. Thus the honest finding is no significant circularity, and the score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

This is a review, so it introduces no fitted parameters and no new entities. The load-bearing assumptions are (1) the accuracy of the cited primary studies and (2) the clinical relevance of standard fusion metrics. Both are asserted rather than demonstrated.

assumptions (2)

domain assumption The surveyed primary studies accurately report their methods and results.
The review is a synthesis of others' results; without independent verification, the summaries rely on the trustworthiness of the cited literature. Some cited sources are preprints (refs 101, 107).
domain assumption Quantitative fusion metrics such as SSIM, PSNR, entropy, and average gradient are valid indicators of diagnostic improvement.
Used throughout Table 7 and Section 7 to summarize improvements, but Section 9.5 admits clinical validation is pending, so this is an unproven premise.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis." pith.science (2026). https://pith.science/paper/TJFPGEGC

@misc{pith2026250514715,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TJFPGEGC}},
  note         = {Machine review of arXiv:2505.14715}
}

read the original abstract

Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF's vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF's broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance.

Figures

Figures reproduced from arXiv: 2505.14715 by the authors.

**Figure 2.** Sequential fusion process of multi-modal medical images demonstrating the [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗

**Figure 3.** Types of multi-modality medical image fusion [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

J-RAS: Mutual Adaptation for Medical Image Segmentation via Contrastive Retrieval-Augmented Joint Optimization
cs.CV 2025-10 conditional novelty 5.0 of 10

Jointly training the retrieval model and the segmentation model improves cardiac MRI segmentation over training the segmentation model alone.

Reference graph

Works this paper leans on

300 extracted references · 80 canonical work pages · cited by 1 Pith paper

[1]

Medicalimagingmodalities,

H.A.Ahmad, H.J.Yu, andC.G.Miller, “Medicalimagingmodalities,” Med.imagingclinicaltrialspp.3–26(2014)

2014
[2]

Introduction of medical imaging modalities,

S. M. S. Islam, M. A. A. Nasim, I. Hossain,et al., “Introduction of medical imaging modalities,” inData Driven ApproachesonMedical Imaging, (Springer, 2023), pp. 1–25

2023
[3]

Review of multimodality medical image fusion using combined transform techniques for clinical application,

B. Rajalingam, R. Priya, and R. Scholar, “Review of multimodality medical image fusion using combined transform techniques for clinical application,” Int. J. Sci. Res. Comput. Sci. Appl. Manag. Stud.7, 1–8 (2018)

2018
[4]

Is ultrasound safe?

J. Joy, I. Cooke, and M. Love, “Is ultrasound safe?” The Obstet. & Gynaecol.8, 222–227 (2006)

2006
[5]

Ultrasound imaging and contrast agents: a safe alternative to mri?

M. H. Wink, H. Wijkstra, J. J. De La Rosette, and C. A. Grimbergen, “Ultrasound imaging and contrast agents: a safe alternative to mri?” Minim. Invasive Ther. & Allied Technol.15, 93–100 (2006)

2006
[6]

Role of multimodality imaging in the diagnosis and management of cardiomyopathies,

S. Ederhy, N. Mansencal, P. Réant,et al., “Role of multimodality imaging in the diagnosis and management of cardiomyopathies,” Arch. cardiovascular diseases112, 615–629 (2019)

2019
[7]

Application of ai in cardiovascular multimodality imaging,

G. Muscogiuri, V. Volpato, R. Cau,etal., “Application of ai in cardiovascular multimodality imaging,” Heliyon8 (2022)

2022
[8]

Contributionandperformanceofmultimodalimaginginthediagnosis and management of cardiac masses,

M.Lemasle,Y.LavieBadie,E.Cariou, etal.,“Contributionandperformanceofmultimodalimaginginthediagnosis and management of cardiac masses,” The Int. J. Cardiovasc. Imaging36, 971–981 (2020)

2020

Show all 300 references

[9]

Diagnosisofneurologicalandnon-neurologicaldisordersviabimodal/multimodalimaging with lanthanide based nanoparticles,

A.GautamandP.Komal,“Diagnosisofneurologicalandnon-neurologicaldisordersviabimodal/multimodalimaging with lanthanide based nanoparticles,” Coord. Chem. Rev.532, 216527 (2025)

2025
[10]

Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders,

J. Rokicki, T. Wolfers, W. Nordhøy,et al., “Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders,” Hum. brain mapping42, 1714–1726 (2021)

2021
[11]

Integrated multimodal imaging in neurodegenerative disease,

M. Grossman, “Integrated multimodal imaging in neurodegenerative disease,” The Lancet Neurol.14, 973–975 (2015)

2015
[12]

Recent advancements in multimodal medical image fusion techniques for better diagnosis: an overview,

M. Haribabu, V. Guruviah, and P. Yogarajah, “Recent advancements in multimodal medical image fusion techniques for better diagnosis: an overview,” Curr. Med. Imaging19, 673–694 (2023)

2023
[13]

An interpretable framework for gastric cancer classification using multi- channel attention mechanisms and transfer learning approach on histopathology images,

M. Zubair, M. Owais, T. Hassan,et al., “An interpretable framework for gastric cancer classification using multi- channel attention mechanisms and transfer learning approach on histopathology images,” Sci. Reports15, 13087 (2025)

2025
[14]

Quantitative multimodality imaging in cancer research and therapy,

T. E. Yankeelov, R. G. Abramson, and C. C. Quarles, “Quantitative multimodality imaging in cancer research and therapy,” Nat. Rev. Clin. Oncol.11, 670–680 (2014)

2014
[15]

V. V. Tuchin, J. Popp, and V. Zakharov,Multimodaloptical diagnosticsof cancer (Springer, 2020)

2020
[16]

Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images,

M. Zubair, M. Owais, T. Mahmood,etal., “Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images,” Sci. Reports14, 22533 (2024)

2024
[17]

Limitationsofctduringpet/ct,

M.J.Gollub,R.Hong,D.M.Sarasohn,andT.Akhurst,“Limitationsofctduringpet/ct,”J.Nucl.Med. 48,1583–1591 (2007)

2007
[18]

Resolution modeling in pet imaging: theory, practice, benefits, and pitfalls,

A. Rahmim, J. Qi, and V. Sossi, “Resolution modeling in pet imaging: theory, practice, benefits, and pitfalls,” Med. physics 40, 064301 (2013)

2013
[19]

A comprehensive computer-aided system for an early-stage diagnosis and classification of diabetic macular edema,

M. Zubair, M. Umair, R. A. Naqvi,etal., “A comprehensive computer-aided system for an early-stage diagnosis and classification of diabetic macular edema,” J. King Saud Univ. Inf. Sci.35, 101719 (2023)

2023
[20]

Automated grading of diabetic macular edema using color retinal photographs,

M. Zubair, J. Ahmad, F. Alqahtani,et al., “Automated grading of diabetic macular edema using color retinal photographs,”in 20222ndInternationalConferenceofSmartSystemsandEmergingTechnologies(SMARTTECH), (IEEE, 2022), pp. 1–6

2022
[21]

Classification of diabetic macular edema and its stages using color fundus image,

M. Zubair, S. A. Khan, and U. U. Yasin, “Classification of diabetic macular edema and its stages using color fundus image,” J. Electron. Sci. Technol.12, 187–190 (2014)

2014
[22]

Automated brain tumor detection using soft computing-based segmentation technique,

M. Zubair, M. Umair, and M. Owais, “Automated brain tumor detection using soft computing-based segmentation technique,”in 20233rdInternationalConferenceonComputingandInformationTechnology(ICCIT),(IEEE,2023), pp. 211–215

2023
[23]

A novel reciprocal domain adaptation neural network for enhanced diagnosis of chronic kidney disease,

S. Iqbal, A. N. Qureshi, M. Alhussein,et al., “A novel reciprocal domain adaptation neural network for enhanced diagnosis of chronic kidney disease,” Expert Syst.42, e13825 (2025)

2025
[24]

Interpretable deep learning classifier using explainable ai for non-small cell lung cancer,

A. Shabbir and M. Zubair, “Interpretable deep learning classifier using explainable ai for non-small cell lung cancer,” in 2024Horizons ofInformationTechnologyand Engineering (HITE), (IEEE, 2024), pp. 1–6

2024
[25]

Medical imaging: from roentgen to the digital revolution, and beyond,

E. Bercovich and M. C. Javitt, “Medical imaging: from roentgen to the digital revolution, and beyond,” Rambam Maimonides medical journal9(2018)

2018
[26]

Advances in medical imaging,

H. Huang, D. R. Aberle, R. Lufkin,et al., “Advances in medical imaging,” Ann. internal medicine112, 203–220 (1990)

1990
[27]

Recentadvancesinmedicalimaging: anatomicalandclinicalapplications,

B.Grignon,L.Mainard,M.Delion, etal.,“Recentadvancesinmedicalimaging: anatomicalandclinicalapplications,” Surg. Radiol. Anat.34, 675–686 (2012)

2012
[28]

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward,

E. Elyan, P. Vuttipittayamongkol, P. Johnston,et al., “Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward,” Artif. Intell. Surg.2, 24–45 (2022)

2022
[29]

Engineering innovations in healthcare: a review of developments in the usa,

O. Ayo-Farai, B. A. Olaide, C. P. Maduka, and C. C. Okongwu, “Engineering innovations in healthcare: a review of developments in the usa,” Eng. Sci. & Technol. J.4, 381–400 (2023)

2023
[30]

Cutting-edge technology and automation in the pathology laboratory,

E. Munari, A. Scarpa, L. Cima,et al., “Cutting-edge technology and automation in the pathology laboratory,” Virchows Arch.484, 555–566 (2024)

2024
[31]

Medical 4.0 technologies for healthcare: Features, capabilities, and applications,

A. Haleem, M. Javaid, R. P. Singh, and R. Suman, “Medical 4.0 technologies for healthcare: Features, capabilities, and applications,” Internet Things Cyber-Physical Syst.2, 12–30 (2022)

2022
[32]

Structural imaging

E. D. Bigler, “Structural imaging.” (2005)

2005
[33]

Pain and functional imaging,

M. Ingvar, “Pain and functional imaging,” Philos. Trans. Royal Soc. London. Ser. B: Biol. Sci.354, 1347–1358 (1999)

1999
[34]

Functionalimagingof‘theoryofmind’,

H.L.GallagherandC.D.Frith, “Functionalimagingof‘theoryofmind’,” Trendscognitivesciences 7, 77–83(2003)

2003
[35]

Computational pathology: challenges and promises for tissue analysis,

T. J. Fuchs and J. M. Buhmann, “Computational pathology: challenges and promises for tissue analysis,” Comput. Med. Imaging Graph.35, 515–530 (2011)

2011
[36]

Fundamentalsofx-rayimagingandradiationsafety,

T.Bashore,“Fundamentalsofx-rayimagingandradiationsafety,”Catheter.cardiovascularinterventions 54,126–135 (2001)

2001
[37]

Revolutionizing infectious disease management in low-resource settings: The impact of rapid diagnostic technologies and portable devices,

A. O. Olatunji, J. A. Olaboye, C. C. Maha,et al., “Revolutionizing infectious disease management in low-resource settings: The impact of rapid diagnostic technologies and portable devices,” Int. J. Appl. Res. Soc. Sci.6, 1417–1432 (2024)

2024
[38]

Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success,

J. H. Thrall, X. Li, Q. Li,et al., “Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success,” J. Am. Coll. Radiol.15, 504–508 (2018)

2018
[39]

Advances in functional x-ray imaging techniques and contrast agents,

H. Chen, M. M. Rogalski, and J. N. Anker, “Advances in functional x-ray imaging techniques and contrast agents,” Phys. Chem. Chem. Phys.14, 13469–13486 (2012)

2012
[40]

Recentprogressinthedevelopmentofa-se/cmossensorsforx-raydetection,

K.Hellier,E.Benard,C.C.Scott, etal.,“Recentprogressinthedevelopmentofa-se/cmossensorsforx-raydetection,” Quantum Beam Sci.5, 29 (2021)

2021
[41]

Development and application of portable, hand-held x-ray fluorescence spectrometers,

G. L. Bosco, “Development and application of portable, hand-held x-ray fluorescence spectrometers,” TrAC Trends Anal. Chem.45, 121–134 (2013)

2013
[42]

Ct scan,

P. R. Patel and O. De Jesus, “Ct scan,” (2021)

2021
[43]

Principles of ct and ct technology,

L. W. Goldman, “Principles of ct and ct technology,” J. nuclear medicine technology35, 115–128 (2007)

2007
[44]

Towardclinicalx-rayphase-contrastct: demonstrationofenhancedsoft-tissue contrast in human specimen,

T.Donath, F.Pfeiffer, O.Bunk,etal., “Towardclinicalx-rayphase-contrastct: demonstrationofenhancedsoft-tissue contrast in human specimen,” Investig. radiology45, 445–452 (2010)

2010
[45]

Computerized transverse axial scanning (tomography): Part 1. description of system,

G. N. Hounsfield, “Computerized transverse axial scanning (tomography): Part 1. description of system,” The Br. journal radiology46, 1016–1022 (1973)

1973
[46]

Dual-and multi-energy ct: principles, technical approaches, and clinical applications,

C. H. McCollough, S. Leng, L. Yu, and J. G. Fletcher, “Dual-and multi-energy ct: principles, technical approaches, and clinical applications,” Radiology276, 637–653 (2015)

2015
[47]

W. R. Webb and C. B. Higgins,Thoracicimaging: pulmonary andcardiovascularradiology(Lippincott Williams & Wilkins, 2011)

2011
[48]

Weishaupt, V

D. Weishaupt, V. D. Köchli, B. Marincek,et al., Howdoes MRI work?: an introduction to the physicsand function of magnetic resonance imaging, vol. 2 (Springer, 2006)

2006
[49]

Divergent whole brain projections from the ventral midbrain in macaques,

M. Zubair, S. R. Murris, K. Isa,etal., “Divergent whole brain projections from the ventral midbrain in macaques,” Cereb. Cortex31, 2913–2931 (2021)

2021
[50]

Rf pulse methods for use with surface coils: Frequency-modulated pulses and parallel transmission,

M. Garwood and K. Uğurbil, “Rf pulse methods for use with surface coils: Frequency-modulated pulses and parallel transmission,” J. Magn. Reson.291, 84–93 (2018)

2018
[51]

Commissioningoftheiseultcea11.7twhole-bodymri: Currentstatus,gradient–magnet interaction tests and first imaging experience,

N.BoulantandL.Quettier,“Commissioningoftheiseultcea11.7twhole-bodymri: Currentstatus,gradient–magnet interaction tests and first imaging experience,” Magn. Reson. Mater. Physics, Biol. Med.36, 175–189 (2023)

2023
[52]

Advances in computational and statistical diffusion mri,

L. J. O’Donnell, A. Daducci, D. Wassermann, and C. Lenglet, “Advances in computational and statistical diffusion mri,” NMR Biomed.32, e3805 (2019)

2019
[53]

Fundamental principles of ultrasound imaging,

G. A. Santoro, G. Di Falco, and G. Santoro, “Fundamental principles of ultrasound imaging,” Benign Anorectal Dis. Diagn. with Endoanal Endorectal Ultrasound New Treat. Options pp. 3–9 (2006)

2006
[54]

Basic principles of ultrasound,

F. O. Walker, “Basic principles of ultrasound,” inNeuromuscular ultrasound, (Elsevier, Philadelphia, 2011), pp. 1–23

2011
[55]

S. K. Bhargava,Principles andpractice of ultrasonography (Jaypee Brothers Medical Publishers, 2020)

2020
[56]

Doppler ultrasound,

H. F. Routh, “Doppler ultrasound,” IEEE Eng. Med. Biol. Mag.15, 31–40 (1996)

1996
[57]

Diagnostic ultrasound,

J. Bamber and M. Tristam, “Diagnostic ultrasound,” The physics medical imaging pp. 319–388 (1988)

1988
[58]

Brain magnetic resonance imaging with contrast dependent on blood oxygenation

S. Ogawa, T.-M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation.” proceedings National Acad. Sci.87, 9868–9872 (1990)

1990
[59]

What we can do and what we cannot do with fmri,

N. K. Logothetis, “What we can do and what we cannot do with fmri,” Nature453, 869–878 (2008)

2008
[60]

fmri at high spatial resolution: implications for bold-models,

J. Goense, Y. Bohraus, and N. K. Logothetis, “fmri at high spatial resolution: implications for bold-models,” Front. computational neuroscience10, 66 (2016)

2016
[61]

Advantages in functional imaging of the brain,

W. Mier and D. Mier, “Advantages in functional imaging of the brain,” Front. human neuroscience9, 249 (2015)

2015
[62]

Theoretical optimization of multi-echo fmri data acquisition,

P. Gowland and R. Bowtell, “Theoretical optimization of multi-echo fmri data acquisition,” Phys. Med. & Biol.52, 1801 (2007)

2007
[63]

Combining fmri with other modalities: multimodal neuroimaging,

L. Lemieux, K. Whittingstall, and K. Uludağ, “Combining fmri with other modalities: multimodal neuroimaging,” fMRI: From Nucl. Spins to Brain Funct. pp. 739–768 (2015)

2015
[64]

Clinical applications and future directions of functional mri,

D. A. Orringer, D. R. Vago, and A. J. Golby, “Clinical applications and future directions of functional mri,” in Seminars inneurology, vol. 32 (Thieme Medical Publishers, 2012), pp. 466–475

2012
[65]

Current challenges in translational and clinical fmri and future directions,

K. Specht, “Current challenges in translational and clinical fmri and future directions,” Front. psychiatry10, 924 (2020)

2020
[66]

Resting-state fmri: a review of methods and clinical applications,

M. H. Lee, C. D. Smyser, and J. S. Shimony, “Resting-state fmri: a review of methods and clinical applications,” Am. J. neuroradiology34, 1866–1872 (2013)

2013
[67]

Multimodal consumer choice prediction using eeg signals and eye tracking,

S. M. Usman, S. Khalid, A. Tanveer,et al., “Multimodal consumer choice prediction using eeg signals and eye tracking,” Front. Comput. Neurosci.18, 1516440 (2025)

2025
[68]

The development of human functional brain networks,

J. D. Power, D. A. Fair, B. L. Schlaggar, and S. E. Petersen, “The development of human functional brain networks,” Neuron 67, 735–748 (2010)

2010
[69]

Structural and functional brain networks: from connections to cognition,

H.-J. Park and K. Friston, “Structural and functional brain networks: from connections to cognition,” Science342, 1238411 (2013)

2013
[70]

Applications of fmri in translational medicine and clinical practice,

P. M. Matthews, G. D. Honey, and E. T. Bullmore, “Applications of fmri in translational medicine and clinical practice,” Nat. Rev. Neurosci.7, 732–744 (2006)

2006
[71]

S. R. Cherry, J. A. Sorenson, and M. E. Phelps,Physicsinnuclear medicine (Soc Nuclear Med, 2013)

2013
[72]

Fundamentals of pet and pet/ct imaging,

S. Basu, T. C. Kwee, S. Surti,et al., “Fundamentals of pet and pet/ct imaging,” Ann. New York Acad. Sci.1228, 1–18 (2011)

2011
[73]

Basic science of pet imaging,

M. M. Khalilet al., “Basic science of pet imaging,” Tech. rep., Springer (2017)

2017
[74]

Positron emission tomography in ischemic heart disease,

B. S. Santos and M. J. Ferreira, “Positron emission tomography in ischemic heart disease,” Revista Portuguesa de Cardiol. 38, 599–608 (2019)

2019
[75]

Limitations of pet for imaging lymphoma,

S. F. Barrington and M. J. O’doherty, “Limitations of pet for imaging lymphoma,” Eur. J. Nucl. Med. Mol. Imaging 30, S117–S127 (2003)

2003
[76]

The limitation of pet imaging for biological adaptive-imrt assessed in animal models,

N. Christian, J. A. Lee, A. Bol,etal., “The limitation of pet imaging for biological adaptive-imrt assessed in animal models,” Radiother. oncology91, 101–106 (2009)

2009
[77]

Acquisition protocol considerations for combined pet/ct imaging,

T. Beyer, G. Antoch, S. Müller,et al., “Acquisition protocol considerations for combined pet/ct imaging,” J. Nucl. Med. 45, 25S–35S (2004)

2004
[78]

Pet/mri hybrid systems,

J. G. Mannheim, A. M. Schmid, J. Schwenck,et al., “Pet/mri hybrid systems,” inSeminars in nuclear medicine, vol. 48 (Elsevier, 2018), pp. 332–347

2018
[79]

Tau pet imaging: present and future directions,

L. Saint-Aubert, L. Lemoine, K. Chiotis,et al., “Tau pet imaging: present and future directions,” Mol. neurodegener- ation 12, 1–21 (2017)

2017
[80]

A review on ai in pet imaging,

K. Matsubara, M. Ibaraki, M. Nemoto,et al., “A review on ai in pet imaging,” Ann. Nucl. Med.36, 133–143 (2022)

2022
[81]

An automated system to distinguish between corona and viral pneumoniachestdiseasesbasedonimageprocessingtechniques,

A. Al-Ghraibah, M. Altayeb, and F. A. Alnaimat, “An automated system to distinguish between corona and viral pneumoniachestdiseasesbasedonimageprocessingtechniques,”Comput.MethodsBiomech.Biomed.Eng.Imaging & Vis.11, 2261575 (2024)

2024
[82]

Advancements in pet care technology: A comprehensive survey,

M. Tauseef, E. Rathod, S. Nandish, and M. Kushal, “Advancements in pet care technology: A comprehensive survey,” in 2024 4th International Conferenceon Data Engineering and Communication Systems(ICDECS), (IEEE, 2024), pp. 1–6

2024
[83]

Physiological imaging of the lung: single-photon- emission computed tomography (spect),

J. Petersson, A. Sánchez-Crespo, S. A. Larsson, and M. Mure, “Physiological imaging of the lung: single-photon- emission computed tomography (spect),” J. applied physiology102, 468–476 (2007)

2007
[84]

A picture of modern tc-99m radiopharmaceuticals: Production, chemistry, and applications in molecular imaging,

A. Boschi, L. Uccelli, and P. Martini, “A picture of modern tc-99m radiopharmaceuticals: Production, chemistry, and applications in molecular imaging,” Appl. Sci.9, 2526 (2019)

2019
[85]

Spectintheyear2000: basicprinciples,

M.W.GrochandW.D.Erwin,“Spectintheyear2000: basicprinciples,”J.Nucl.Med.Technol. 28,233–244(2000)

2000
[86]

Absolute quantitation of cardiac 99mtc-pyrophosphate using cadmium- zinc-telluride–based spect/ct,

S. Dorbala, M.-A. Park, S. Cuddy,et al., “Absolute quantitation of cardiac 99mtc-pyrophosphate using cadmium- zinc-telluride–based spect/ct,” J. Nucl. Med.62, 716–722 (2021)

2021
[87]

Recent developments in spect/ct,

P. Ritt, “Recent developments in spect/ct,” inSeminarsinNuclearMedicine, vol. 52 (Elsevier, 2022), pp. 276–285

2022
[88]

D. W. McRobbie, E. A. Moore, M. J. Graves, and M. R. Prince,MRIfromPicturetoProton(Cambridge university press, 2017)

2017
[89]

Pet/ct today and tomorrow,

D. W. Townsend, J. P. Carney, J. T. Yap, and N. C. Hall, “Pet/ct today and tomorrow,” J. Nucl. Med.45, 4S–14S (2004)

2004
[90]

Onthelimitationsandoptimisationofhigh-resolution3dmedicalx-rayimagingsystems,

S.-A.ZhouandA.Brahme,“Onthelimitationsandoptimisationofhigh-resolution3dmedicalx-rayimagingsystems,” Nucl. Instruments Methods Phys. Res. Sect. A: Accel. Spectrometers, Detect. Assoc. Equip.648, S284–S287 (2011)

2011
[91]

Functional mr imaging: capabilities and limitations,

M. E. Moseley and G. H. Glover, “Functional mr imaging: capabilities and limitations,” Neuroimaging Clin. North Am. 5, 161–191 (1995)

1995
[92]

Functionalimagingstudiesofneuropsychologicalpatients: applicationsandlimitations,

C.J.PriceandK.J.Friston,“Functionalimagingstudiesofneuropsychologicalpatients: applicationsandlimitations,” Neurocase 8, 345–354 (2002)

2002
[93]

Mdc-rht: Multi-modal medical image fusion via multi-dimensional dynamic convolution and residual hybrid transformer,

W. Wang, J. He, H. Liu, and W. Yuan, “Mdc-rht: Multi-modal medical image fusion via multi-dimensional dynamic convolution and residual hybrid transformer,” Sensors24, 4056 (2024)

2024
[94]

Enhanced multimodal medical image fusion via modified dwt with arithmetic optimization algorithm,

A. A. Alzahrani, “Enhanced multimodal medical image fusion via modified dwt with arithmetic optimization algorithm,” Sci. Reports14, 19261 (2024)

2024
[95]

Multimodal medical image fusion using adaptive co-occurrence filter-based decomposition optimization model,

R. Zhu, X. Li, S. Huang, and X. Zhang, “Multimodal medical image fusion using adaptive co-occurrence filter-based decomposition optimization model,” Bioinformatics38, 818–826 (2022)

2022
[96]

An improved multimodal medical image fusion approach using intuitionistic fuzzy set and intuitionistic fuzzy cross-correlation,

M. Haribabu and V. Guruviah, “An improved multimodal medical image fusion approach using intuitionistic fuzzy set and intuitionistic fuzzy cross-correlation,” Diagnostics13, 2330 (2023)

2023
[97]

Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [18f] psma-1007 pet/ct and multiparametric mri,

H. Lin, F. Yao, X. Yi,et al., “Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [18f] psma-1007 pet/ct and multiparametric mri,” Eur. J. Nucl. Med. Mol. Imaging pp. 1–12 (2025)

2025
[98]

Mfcnet: A multi-modal fusion and calibration networks for 3d pancreas tumor segmentation on pet-ct images,

F. Wang, C. Cheng, W. Cao,etal., “Mfcnet: A multi-modal fusion and calibration networks for 3d pancreas tumor segmentation on pet-ct images,” Comput. Biol. Med.155, 106657 (2023)

2023
[99]

A fusion algorithm based on composite decomposition for pet and mri medical images,

J. Zhou, X. Xing, M. Yan,et al., “A fusion algorithm based on composite decomposition for pet and mri medical images,” Biomed. Signal Process. Control.76, 103717 (2022)

2022
[100]

Clinical application of pet/mri in oncology,

H. Sotoudeh, A. Sharma, K. J. Fowler,etal., “Clinical application of pet/mri in oncology,” J. Magn. Reson. Imaging 44, 265–276 (2016)

2016
[101]

Integrating ultrasound-ct-mr for preoperative multi-task prediction in ovarian cancer: Achieving diagnostic parity with multidisciplinary team consensus,

J. Yu, P. Hu, R. Dai,etal., “Integrating ultrasound-ct-mr for preoperative multi-task prediction in ovarian cancer: Achieving diagnostic parity with multidisciplinary team consensus,” SSRN (2025)

2025
[102]

A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma,

F. Wang, Q. Chen, Y. Chen,et al., “A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma,” Eur. J. Surg. Oncol.49, 156–164 (2023)

2023
[103]

Fusion of magnetic resonance and ultrasound images using guided filtering: Application to endometriosis surgery,

Y. El Bennioui, A. Halimi, A. Basarab, and J.-Y. Tourneret, “Fusion of magnetic resonance and ultrasound images using guided filtering: Application to endometriosis surgery,” in202432ndEuropeanSignalProcessingConference (EUSIPCO), (IEEE, 2024), pp. 1631–1635

2024
[104]

Real-time ultrasound and freehand-spect,

M. Freesmeyer, T. Winkens, T. Opfermann,et al., “Real-time ultrasound and freehand-spect,” Nuklearmedizin- NuclearMedicine 53, 259–264 (2014)

2014
[105]

Smmf: a self-attention-based multi-parametric mri feature fusion framework for the diagnosis of bladder cancer grading,

T. Tao, Y. Chen, Y. Shang,etal., “Smmf: a self-attention-based multi-parametric mri feature fusion framework for the diagnosis of bladder cancer grading,” Front. Oncol.14, 1337186 (2024)

2024
[106]

Group-sparse representation with dictionary learning for medical image denoising and fusion,

S. Li, H. Yin, and L. Fang, “Group-sparse representation with dictionary learning for medical image denoising and fusion,” IEEE Trans. on biomedical engineering59, 3450–3459 (2012)

2012
[107]

Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal mri,

L. W. Remedios, H. Liu, S. W. Remedios,et al., “Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal mri,” arXiv preprint arXiv:2409.04563 (2024)

2024 arXiv
[108]

Deep-learning framework to detect lung abnormality–a study with chest x-ray and lung ct scan images,

A. Bhandary, G. A. Prabhu, V. Rajinikanth,et al., “Deep-learning framework to detect lung abnormality–a study with chest x-ray and lung ct scan images,” Pattern Recognit. Lett.129, 271–278 (2020)

2020
[109]

Multimodal fusion learning with dual attention for medical imaging,

J. Dhar, N. Zaidi, M. Haghighat,etal., “Multimodal fusion learning with dual attention for medical imaging,” in 2025 IEEE/CVFWinterConferenceon Applications ofComputer Vision(WACV), (IEEE, 2025), pp. 4362–4371

2025
[110]

3d fusion of functional cardiac magnetic resonance imaging and computed tomography coronary angiography: accuracy and added clinical value,

O. F. Donati, H. Alkadhi, H. Scheffel,et al., “3d fusion of functional cardiac magnetic resonance imaging and computed tomography coronary angiography: accuracy and added clinical value,” Investig. radiology46, 331–340 (2011)

2011
[111]

Combined positron emission tomography–computed tomography: the historical perspective,

D. W. Townsend, “Combined positron emission tomography–computed tomography: the historical perspective,” in Seminars inUltrasound,CTand MRI, vol. 29 (Elsevier, 2008), pp. 232–235

2008
[112]

Mri and ultrasound fusion imaging for prenatal diagnosis,

L. J. Salomon, J.-P. Bernard, A.-E. Millischer,et al., “Mri and ultrasound fusion imaging for prenatal diagnosis,” Am. journal obstetrics gynecology209, 148–e1 (2013)

2013
[113]

Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter,

J. H. Siewerdsen and D. A. Jaffray, “Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter,” Med. physics28, 220–231 (2001)

2001
[114]

Pixel-level image fusion: A survey of the state of the art,

S. Li, X. Kang, L. Fang,et al., “Pixel-level image fusion: A survey of the state of the art,” information Fusion33, 100–112 (2017)

2017
[115]

Pixel level image fusion—a review on various techniques,

P. Jagalingam and A. V. Hegde, “Pixel level image fusion—a review on various techniques,” in3rd WorldConf. on Applied Sciences,Engineering andTechnology, (2014)

2014
[116]

Remote sensing image fusion with deep convolutional neural network,

Z. Shao and J. Cai, “Remote sensing image fusion with deep convolutional neural network,” IEEE journal selected topics applied earth observations remote sensing11, 1656–1669 (2018)

2018
[117]

An efficient frequency domain fusion network of infrared and visible images,

C. Wang, J. Wu, A. Fang,etal., “An efficient frequency domain fusion network of infrared and visible images,” Eng. Appl. Artif. Intell.133, 108013 (2024)

2024
[118]

Pixel-level structure awareness for enhancing multi-modal medical image fusion,

L. Wei, R. Zhu, X. Li,et al., “Pixel-level structure awareness for enhancing multi-modal medical image fusion,” Biomed. Signal Process. Control.97, 106694 (2024)

2024
[119]

Hybrid pixel-feature fusion system for multimodal medical images,

N. Tawfik, H. A. Elnemr, M. Fakhr,etal., “Hybrid pixel-feature fusion system for multimodal medical images,” J. Ambient Intell. Humaniz. Comput.12, 6001–6018 (2021)

2021
[120]

A hybridwavelet-shearlet approach to robust digital imagewatermarking,

A. B. Hassanat, V. S. Prasath, K. I. Mseidein,et al., “A hybridwavelet-shearlet approach to robust digital imagewatermarking,” Informatica41 (2017)

2017
[121]

An adaptive parameter tuning of particle swarm optimization algorithm,

G. Xu, “An adaptive parameter tuning of particle swarm optimization algorithm,” Appl. Math. Comput.219, 4560–4569 (2013)

2013
[122]

Rfn-nest: An end-to-end residual fusion network for infrared and visible images,

H. Li, X.-J. Wu, and J. Kittler, “Rfn-nest: An end-to-end residual fusion network for infrared and visible images,” Inf. Fusion73, 72–86 (2021)

2021
[123]

Multi-focus image fusion with a deep convolutional neural network,

Y. Liu, X. Chen, H. Peng, and Z. Wang, “Multi-focus image fusion with a deep convolutional neural network,” Inf. Fusion 36, 191–207 (2017)

2017
[124]

Fuzzy logic based image fusion,

T. Meitzler, D. Bednarz, E. Sohn,et al., “Fuzzy logic based image fusion,” US Army TACON, Tech. Rep13818 (2002)

2002
[125]

The new segmentation and fuzzy logic based multi-sensor image fusion,

J. Saeedi and K. Faez, “The new segmentation and fuzzy logic based multi-sensor image fusion,” in2009 24th International ConferenceImageandVisionComputing NewZealand, (IEEE, 2009), pp. 328–333

2009
[126]

Optimum spectrum mask based medical image fusion using gray wolf optimization,

E. Daniel, J. Anitha, K. Kamaleshwaran, and I. Rani, “Optimum spectrum mask based medical image fusion using gray wolf optimization,” Biomed. Signal Process. Control.34, 36–43 (2017)

2017
[127]

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion: introduction,

Y. Jiang and Y. Ma, “Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion: introduction,” Ann. Transl. Med.8 (2020)

2020
[128]

Artificial bee colony and its application for image fusion,

P. K. Sharma, V. Bhavya, K. Navyashree,etal., “Artificial bee colony and its application for image fusion,” IJ Inf. Technol. Comput. Sci.11, 42–49 (2012)

2012
[129]

Artificial bee colony approach to information granulation-based fuzzy radial basis function neural networks for image fusion,

J. Yu and H. Duan, “Artificial bee colony approach to information granulation-based fuzzy radial basis function neural networks for image fusion,” Optik-International J. for Light Electron Opt.124, 3103–3111 (2013)

2013
[130]

Variants of artificial bee colony algorithm and its applications in medical image processing,

Ş. Öztürk, R. Ahmad, and N. Akhtar, “Variants of artificial bee colony algorithm and its applications in medical image processing,” Appl. soft computing97, 106799 (2020)

2020
[131]

Anewimprovedhsvimagefusionmethod,

L.DingandH.Li,“Anewimprovedhsvimagefusionmethod,”in LIDARImagingDetectionandTargetRecognition 2017, vol. 10605 (SPIE, 2017), pp. 426–438

2017
[132]

Fusionofvisibleandinfraredimagesinhsvcolorspace,

M.ManchandaandR.Sharma, “Fusionofvisibleandinfraredimagesinhsvcolorspace,” in 20173rdInternational Conferenceon Computational Intelligence&Communication Technology(CICT), (IEEE, 2017), pp. 1–6

2017
[133]

Digital image fusion using adaptive neuro-fuzzy inference system,

S. Shukla and R. Raja, “Digital image fusion using adaptive neuro-fuzzy inference system,” Int. J. New Technol. Res. 2, 263508 (2016)

2016
[134]

Gan review: Models and medical image fusion applications,

T. Zhou, Q. Li, H. Lu,etal., “Gan review: Models and medical image fusion applications,” Inf. Fusion91, 134–148 (2023)

2023
[135]

Guided image filtering,

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE transactions on pattern analysis machine intelligence35, 1397–1409 (2012)

2012
[136]

Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter,

W. Gan, X. Wu, W. Wu,et al., “Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter,” Infrared Phys. & Technol.72, 37–51 (2015)

2015
[137]

Multi-focus image fusion with joint guided image filtering,

Y. Zhang, P. Zhao, Y. Ma, and X. Fan, “Multi-focus image fusion with joint guided image filtering,” Signal Process. Image Commun.92, 116128 (2021)

2021
[138]

Multi-modal sensor medical image fusion based on multiple salient features with guided image filter,

W. Li, L. Jia, and J. Du, “Multi-modal sensor medical image fusion based on multiple salient features with guided image filter,” Ieee Access7, 173019–173033 (2019)

2019
[139]

Gradient domain guided image filtering,

F. Kou, W. Chen, C. Wen, and Z. Li, “Gradient domain guided image filtering,” IEEE Trans. on Image Process.24, 4528–4539 (2015)

2015
[140]

Multimodal medical image fusion using rolling guidance filter with cnn and nuclear norm minimization,

S. Liu, L. Yin, S. Miao,et al., “Multimodal medical image fusion using rolling guidance filter with cnn and nuclear norm minimization,” Curr. Med. Imaging Rev.16, 1243–1258 (2020)

2020
[141]

Multimodal biomedical image fusion method via rolling guidance filter and deep convolutional neural networks,

J. Fu, W. Li, A. Ouyang, and B. He, “Multimodal biomedical image fusion method via rolling guidance filter and deep convolutional neural networks,” Optik237, 166726 (2021)

2021
[142]

Adaptive infrared and visible image fusion method by using rolling guidance filter and saliency detection,

Y. Lin, D. Caoet al., “Adaptive infrared and visible image fusion method by using rolling guidance filter and saliency detection,” Optik262, 169218 (2022)

2022
[143]

Multi-scale image fusion through rolling guidance filter,

L. Jian, X. Yang, Z. Zhou,et al., “Multi-scale image fusion through rolling guidance filter,” Future Gener. Comput. Syst. 83, 310–325 (2018)

2018
[144]

New spiking cortical model for invariant texture retrieval and image processing,

K. Zhan, H. Zhang, and Y. Ma, “New spiking cortical model for invariant texture retrieval and image processing,” IEEE Trans. on Neural Networks20, 1980–1986 (2009)

2009
[145]

Infrared and visible image fusion algorithm based on gaussian fuzzy logic and adaptive dual-channel spiking cortical model,

L. Wen, Y. Kuntao, S. Leilei, and L. Sheng, “Infrared and visible image fusion algorithm based on gaussian fuzzy logic and adaptive dual-channel spiking cortical model,” Infrared Technol.44, 693–701 (2022)

2022
[146]

Guided filter random walk and improved spiking cortical model based image fusion method in nsst domain,

W. Kong, Q. Miao, Y. Lei, and C. Ren, “Guided filter random walk and improved spiking cortical model based image fusion method in nsst domain,” Neurocomputing488, 509–527 (2022)

2022
[147]

Rolling guidance filter,

Q. Zhang, X. Shen, L. Xu, and J. Jia, “Rolling guidance filter,” inComputer Vision–ECCV2014: 13th European Conference,Zurich, Switzerland,September6-12, 2014,Proceedings, Part III 13, (Springer, 2014), pp. 815–830

2014
[148]

Integration of adaptive guided filtering, deep feature learning, and edge-detection techniques for hyperspectral image classification,

X. Wan, C. Zhao, and B. Gao, “Integration of adaptive guided filtering, deep feature learning, and edge-detection techniques for hyperspectral image classification,” Opt. Eng.56, 113106–113106 (2017)

2017
[149]

Colliding depths and fusion: Leveraging adaptive feature maps and restorable depth recharge for infrared and visible scene fusion,

M. Han, K. Yu, W. Li,et al., “Colliding depths and fusion: Leveraging adaptive feature maps and restorable depth recharge for infrared and visible scene fusion,” IEEE Trans. on Multimed. (2024)

2024
[150]

Significant feature suppression and cross-feature fusion networks for fine-grained visual classification,

S. Yang, X. Yang, J. Wu, and B. Feng, “Significant feature suppression and cross-feature fusion networks for fine-grained visual classification,” Sci. Reports14, 24051 (2024)

2024
[151]

Feature fusion methods in deep-learning generic object detection: A survey,

J. Deng, S. Bei, S. Shaojing, and Z. Zhen, “Feature fusion methods in deep-learning generic object detection: A survey,” in2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), vol. 9 (IEEE, 2020), pp. 431–437

2020
[152]

Fusionmamba: Dynamic feature enhancement for multimodal image fusion with mamba,

X. Xie, Y. Cui, T. Tan,et al., “Fusionmamba: Dynamic feature enhancement for multimodal image fusion with mamba,” Vis. Intell.2, 37 (2024)

2024
[153]

Superpixel guided spectral-spatial feature extraction and weighted feature fusion for hyperspectral image classification with limited training samples,

Y. Li, L. Zhang, L. Chen, and Y. Ma, “Superpixel guided spectral-spatial feature extraction and weighted feature fusion for hyperspectral image classification with limited training samples,” Sci. Reports15, 3473 (2025)

2025
[154]

A dynamic receptive field and improved feature fusion approach for federated learning in financial credit risk assessment,

R. Li, Y. Cao, Y. Shu,etal., “A dynamic receptive field and improved feature fusion approach for federated learning in financial credit risk assessment,” Sci. Reports14, 26515 (2024)

2024
[155]

Feature-decision level collaborative fusion network for hyperspectral and lidar classification,

S. Zhang, X. Meng, Q. Liu,et al., “Feature-decision level collaborative fusion network for hyperspectral and lidar classification,” Remote. Sens.15, 4148 (2023)

2023
[156]

Multi-level feature fusion in cnn-based human action recognition: A case study on efficientnet-b7,

P. Lueangwitchajaroen, S. Watcharapinchai, W. Tepsan, and S. Sooksatra, “Multi-level feature fusion in cnn-based human action recognition: A case study on efficientnet-b7,” J. Imaging10, 320 (2024)

2024
[157]

Model agnostic meta-learning (maml)-based ensemble model for accurate detection of wheat diseases using vision transformer and graph neural networks

Y. Maqsood, S. M. Usman, M. Alhussein,et al., “Model agnostic meta-learning (maml)-based ensemble model for accurate detection of wheat diseases using vision transformer and graph neural networks.” Comput. Mater. & Continua 79 (2024)

2024
[158]

Methodsofcombiningmultipleclassifiersandtheirapplicationstohandwriting recognition,

L.Xu,A.Krzyzak,andC.Y.Suen,“Methodsofcombiningmultipleclassifiersandtheirapplicationstohandwriting recognition,” IEEE transactions on systems, man, cybernetics22, 418–435 (1992)

1992
[159]

Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes,

Z. Guo, X. Li, H. Huang,etal., “Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes,” in2018IEEE15thInternational SymposiumonBiomedicalImaging(ISBI2018), (IEEE, 2018), pp. 903–907

2018
[160]

Feature selection for classification of hyperspectral data by svm,

M. Pal and G. M. Foody, “Feature selection for classification of hyperspectral data by svm,” IEEE Trans. on Geosci. Remote. Sens.48, 2297–2307 (2010)

2010
[161]

Sensitivity of support vector machines to random feature selection in classification of hyperspectral data,

B. Waske, S. van der Linden, J. A. Benediktsson,etal., “Sensitivity of support vector machines to random feature selection in classification of hyperspectral data,” IEEE Trans. on Geosci. Remote. Sens.48, 2880–2889 (2010)

2010
[162]

Emotion recognition from occluded facial images using deep ensemble model,

Z. Ullah, M. I. Mohmand, S. U. Rehman,et al., “Emotion recognition from occluded facial images using deep ensemble model,” Cmc-Computers Mater. & Continua73, 4465–4487 (2022)

2022
[163]

Deep learning decision fusion for the classification of urban remote sensing data,

G. Abdi, F. Samadzadegan, and P. Reinartz, “Deep learning decision fusion for the classification of urban remote sensing data,” J. Appl. Remote. Sens.12, 016038–016038 (2018)

2018
[164]

An overview on pixel-level image fusion in remote sensing,

Y. Yang, C. Han, X. Kang, and D. Han, “An overview on pixel-level image fusion in remote sensing,” in2007 ieee international conferenceonautomation and logistics, (IEEE, 2007), pp. 2339–2344

2007
[165]

Pixel-level image fusion using wavelets and principal component analysis,

V. Naidu and J. R. Raol, “Pixel-level image fusion using wavelets and principal component analysis,” Def. science journal 58, 338–352 (2008)

2008
[166]

Evaluation of image fusion of multi focus images in spatial and frequency domain,

B. P. Goud, B. Sushmita, and A. Vijitha, “Evaluation of image fusion of multi focus images in spatial and frequency domain,” Int. J. Comput. Eng. Res. (IJCER)8, 2250–3005 (2018)

2018
[167]

Two-scale multimodal medical image fusion based on structure preservation,

S. Liu, M. Wang, L. Yin,et al., “Two-scale multimodal medical image fusion based on structure preservation,” Front. Comput. Neurosci.15, 803724 (2022)

2022
[168]

A novel method of multimodal medical image fusion using fuzzy transform,

M. Manchanda and R. Sharma, “A novel method of multimodal medical image fusion using fuzzy transform,” J. Vis. Commun. Image Represent.40, 197–217 (2016)

2016
[169]

Animprovedmultimodalmedicalimagefusionalgorithmbasedonfuzzytransform,

M.ManchandaandR.Sharma,“Animprovedmultimodalmedicalimagefusionalgorithmbasedonfuzzytransform,” J. Vis. Commun. Image Represent.51, 76–94 (2018)

2018
[170]

A phase congruency and local laplacian energy based multi-modality medical image fusion method in nsct domain,

Z. Zhu, M. Zheng, G. Qi,et al., “A phase congruency and local laplacian energy based multi-modality medical image fusion method in nsct domain,” Ieee Access7, 20811–20824 (2019)

2019
[171]

Directive contrast based multimodal medical image fusion in nsct domain,

G. Bhatnagar, Q. J. Wu, and Z. Liu, “Directive contrast based multimodal medical image fusion in nsct domain,” IEEE transactions on multimedia15, 1014–1024 (2013)

2013
[172]

Ct and mr image fusion scheme in nonsubsampled contourlet transform domain,

P. Ganasala and V. Kumar, “Ct and mr image fusion scheme in nonsubsampled contourlet transform domain,” J. digital imaging27, 407–418 (2014)

2014
[173]

Mri-pet image fusion based on nsct transform using local energy and local variance fusion rules,

N. Amini, E. Fatemizadeh, and H. Behnam, “Mri-pet image fusion based on nsct transform using local energy and local variance fusion rules,” J. medical engineering & technology38, 211–219 (2014)

2014
[174]

Medical image fusion based on sparse representation and pcnn in nsct domain,

J. Xia, Y. Chen, A. Chen, and Y. Chen, “Medical image fusion based on sparse representation and pcnn in nsct domain,” Comput. mathematical methods medicine2018, 2806047 (2018)

2018
[175]

Multiscaled combination of mr and spect images in neuroimaging: a simplex method based variable-weight fusion,

T. Li and Y. Wang, “Multiscaled combination of mr and spect images in neuroimaging: a simplex method based variable-weight fusion,” Comput. methods programs biomedicine105, 31–39 (2012)

2012
[176]

A hybrid multi-focus medical image fusion based on wavelet transform,

M. Chandana, S. Amutha, and N. Kumar, “A hybrid multi-focus medical image fusion based on wavelet transform,” Int. J. Res. Rev. Comput. Sci.2, 948 (2011)

2011
[177]

Fusion of medical images in wavelet domain: a hybrid implementation,

S. P. Yadav and S. Yadav, “Fusion of medical images in wavelet domain: a hybrid implementation,” Comput. Model. Eng. & Sci.122, 303–321 (2020)

2020
[178]

Pixel based medical image fusion techniques using discrete wavelet transform and stationary wavelet transform,

K. Indira, R. R. Hemamalini, and R. Indhumathi, “Pixel based medical image fusion techniques using discrete wavelet transform and stationary wavelet transform,” Indian J. Sci. Technol.8, 1–7 (2015)

2015
[179]

Evaluation of choose max and contrast based fusion rule using dwt for pet, ct images,

K. Indira and R. R. Hemamalini, “Evaluation of choose max and contrast based fusion rule using dwt for pet, ct images,” Indian J. Sci. Technol.8, 1–4 (2015)

2015
[180]

Multiscale medical image fusion in wavelet domain,

R. Singh and A. Khare, “Multiscale medical image fusion in wavelet domain,” The Sci. World J.2013, 521034 (2013)

2013
[181]

Medical image fusion using discrete fractional wavelet transform,

X. Xu, Y. Wang, and S. Chen, “Medical image fusion using discrete fractional wavelet transform,” Biomed. signal processing control27, 103–111 (2016)

2016
[182]

Optimumwavelet-basedhomomorphicmedicalimagefusionusinghybridgenetic–greywolfoptimization algorithm,

E.Daniel,“Optimumwavelet-basedhomomorphicmedicalimagefusionusinghybridgenetic–greywolfoptimization algorithm,” IEEE Sensors J.18, 6804–6811 (2018)

2018
[183]

Performance comparison of different wavelet based image fusion techniques for lumbar spine images,

M. Nanavati and M. Shah, “Performance comparison of different wavelet based image fusion techniques for lumbar spine images,” J. Integr. Sci. Technol.12, 703–703 (2024)

2024
[184]

Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains,

V. Bhateja, H. Patel, A. Krishn,et al., “Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains,” IEEE Sensors J.15, 6783–6790 (2015)

2015
[185]

Multimodalmedicalimagefusionofmri-petusingwavelettransform,

M.Haribabu,C.H.Bindu,andK.S.Prasad,“Multimodalmedicalimagefusionofmri-petusingwavelettransform,” in 2012 International Conference on Advances in Mobile Network, Communication and Its Applications, (IEEE, 2012), pp. 127–130

2012
[186]

Mri and spect medical image fusion using wavelet transform,

B. S. N. Rao, N. V. K. Raju, M. Dhanush,et al., “Mri and spect medical image fusion using wavelet transform,” in 2022 7th International Conference on Communication and Electronics Systems (ICCES), (IEEE, 2022), pp. 1690–1696

2022
[187]

Mri and ct image fusion based on wavelet transform,

H. O. S. Mishra and S. Bhatnagar, “Mri and ct image fusion based on wavelet transform,” Int. J. Inf. Comput. Technol. 4, 47–52 (2014)

2014
[188]

A hybrid guided filtering and transform-based sparse representation framework for fusion of multimodal medical images,

S. Sandhya, M. Senthil Kumar, and B. Chidhambararajan, “A hybrid guided filtering and transform-based sparse representation framework for fusion of multimodal medical images,” inInternational Conference on Futuristic Communication and NetworkTechnologies, (Springer, 2021), pp. 267–274

2021
[189]

Fusionofmultisensorimagesusingnonsubsampledcontourlettransform and fuzzy logic,

D.E.Nirmala,R.Vignesh,andV.Vaidehi,“Fusionofmultisensorimagesusingnonsubsampledcontourlettransform and fuzzy logic,” in2013IEEE International Conferenceon FuzzySystems(FUZZ-IEEE), (IEEE, 2013), pp. 1–8

2013
[190]

Decision based fuzzy logic approach for multimodal medical image fusion in nsct domain,

S. Sivasangumani, “Decision based fuzzy logic approach for multimodal medical image fusion in nsct domain,” Int. J. Image Min.3, 117–138 (2018)

2018
[191]

Brain image fusion using the parameter adaptive-pulse coupled neural network (pa-pcnn) and non-subsampled contourlet transform (nsct),

S. I. Ibrahim, G. S. El-Tawel, and M. Makhlouf, “Brain image fusion using the parameter adaptive-pulse coupled neural network (pa-pcnn) and non-subsampled contourlet transform (nsct),” Multimed. Tools Appl.83, 27379–27409 (2024)

2024
[192]

Enhanced multimodal medical image fusion based on pythagorean fuzzy set: an innovative approach,

M. Haribabu and V. Guruviah, “Enhanced multimodal medical image fusion based on pythagorean fuzzy set: an innovative approach,” Sci. Reports13, 16726 (2023)

2023
[193]

Multimodal medical image fusion using non- subsampled shearlet transform and pulse coupled neural network incorporated with morphological gradient,

S. D. Ramlal, J. Sachdeva, C. K. Ahuja, and N. Khandelwal, “Multimodal medical image fusion using non- subsampled shearlet transform and pulse coupled neural network incorporated with morphological gradient,” Signal, Image Video Process.12, 1479–1487 (2018)

2018
[194]

Feature-motivated simplified adaptive pcnn-based medical image fusion algorithm in nsst domain,

P. Ganasala and V. Kumar, “Feature-motivated simplified adaptive pcnn-based medical image fusion algorithm in nsst domain,” J. digital imaging29, 73–85 (2016)

2016
[195]

Nsst domain ct–mr neurological image fusion using optimised biologically inspired neural network,

M. Das, D. Gupta, P. Radeva, and A. M. Bakde, “Nsst domain ct–mr neurological image fusion using optimised biologically inspired neural network,” IET Image Process.14, 4291–4305 (2020)

2020
[196]

Multimodal brain image fusion based on improved rolling guidance filter and wiener filter,

H. Lin, Y. Song, H. Wang,et al., “Multimodal brain image fusion based on improved rolling guidance filter and wiener filter,” Comput. Math. Methods Med.2022, 5691099 (2022)

2022
[197]

Guided filter-based images fusion algorithm for ct and mri medical images,

Y. Na, L. Zhao, Y. Yang, and M. Ren, “Guided filter-based images fusion algorithm for ct and mri medical images,” IET Image Process.12, 138–148 (2018)

2018
[198]

Contrast enhanced multi sensor image fusion based on guided image filter and nsst,

P. Ganasala and A. Prasad, “Contrast enhanced multi sensor image fusion based on guided image filter and nsst,” IEEE Sensors J.20, 939–946 (2019)

2019
[199]

Image dehazing based on sigmoid-guided filtering-retinex in nsct domain

Y. Wang, B. Wu, and Z. Duan, “Image dehazing based on sigmoid-guided filtering-retinex in nsct domain.” IAENG Int. J. Appl. Math.54(2024)

2024
[200]

An efficient medical assistive diagnostic algorithm for visualisation of structural and tissue details in ct and mri fusion,

B. Goyal, A. Dogra, R. Khoond, and F. Al-Turjman, “An efficient medical assistive diagnostic algorithm for visualisation of structural and tissue details in ct and mri fusion,” Cogn. Comput.13, 1471–1483 (2021)

2021
[201]

Feature level fusion of multimodal medical images in lifting wavelet transform domain,

S. Kor and U. Tiwary, “Feature level fusion of multimodal medical images in lifting wavelet transform domain,” in The26thAnnualInternational ConferenceoftheIEEEEngineering inMedicineandBiologySociety,vol. 1 (IEEE, 2004), pp. 1479–1482

2004
[202]

Feature-based image fusion with a uniform discrete curvelet transform,

L. Xu, J. Du, Q. Hu, and Q. Li, “Feature-based image fusion with a uniform discrete curvelet transform,” Int. J. Adv. Robotic Syst.10, 255 (2013)

2013
[203]

Udct: lung cancer detection and classification using u-net and darts for medical ct images,

A. Gupta, A. Kumar, and K. Rautela, “Udct: lung cancer detection and classification using u-net and darts for medical ct images,” Multimed. Tools Appl. pp. 1–21 (2024)

2024
[204]

Feature-level image fusion through consistent region segmentation and dual-tree complex wavelet transform

T. Wei, Q. Gao, N. Ma,et al., “Feature-level image fusion through consistent region segmentation and dual-tree complex wavelet transform.” J. Imaging Sci. & Technol.60 (2016)

2016
[205]

A survey on region based image fusion methods,

B. Meher, S. Agrawal, R. Panda, and A. Abraham, “A survey on region based image fusion methods,” Inf. Fusion 48, 119–132 (2019)

2019
[206]

Infrared and visible image fusion based on nsst and phase consistency adaptive dual channel pcnn,

Q. Xie, L. Ma, Z. Guo,etal., “Infrared and visible image fusion based on nsst and phase consistency adaptive dual channel pcnn,” Infrared Phys. & Technol.131, 104659 (2023)

2023
[207]

Nsst based mri-pet/spect color image fusion using local features fuzzy rules and nsml in yiq space,

H. Ullah, Y. Zhao, L. Wu,et al., “Nsst based mri-pet/spect color image fusion using local features fuzzy rules and nsml in yiq space,” in2019 IEEE International Symposium on Signal Processing and InformationTechnology (ISSPIT), (IEEE, 2019), pp. 1–6

2019
[208]

Image fusion algorithm based on improved k-singular value decomposition and hadamard measurement matrix,

Q. Yang and H. Wang, “Image fusion algorithm based on improved k-singular value decomposition and hadamard measurement matrix,” J. Algorithms & Comput. Technol.13, 1748301818791511 (2018)

2018
[209]

Brain image fusion approach based on side window filtering,

A. A. Smadi, A. Abugabah, A. Mehmood, and S. Yang, “Brain image fusion approach based on side window filtering,” Procedia Comput. Sci.198, 295–300 (2022)

2022
[210]

An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images,

N. Zhang, J. Liu, Y. Jin,etal., “An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images,” BMC bioinformatics24, 315 (2023)

2023
[211]

Surveystudyofmultimodalitymedicalimagefusionmethods,

N.Tawfik,H.A.Elnemr,M.Fakhr, etal.,“Surveystudyofmultimodalitymedicalimagefusionmethods,”Multimed. Tools Appl.80, 6369–6396 (2021)

2021
[212]

The fast wavelet transform: Beyond fourier transforms,

M. A. Cody, “The fast wavelet transform: Beyond fourier transforms,” Dr. Dobb’s J.17 (1992)

1992
[213]

Grey wolf optimizer and other metaheuristic optimization techniques with image processing as their applications: a review,

A. Kumar, S. Singh, A. Kumaretal., “Grey wolf optimizer and other metaheuristic optimization techniques with image processing as their applications: a review,” inIOP ConferenceSeries: Materials Science and Engineering, vol. 1136 (IOP Publishing, 2021), p. 012053

2021
[214]

Fusion of pet and mr brain images based on ihs and log-gabor transforms,

C.-I. Chen, “Fusion of pet and mr brain images based on ihs and log-gabor transforms,” IEEE Sensors J.17, 6995–7010 (2017)

2017
[215]

Pulmonary nodule detection model based on svm and ct image feature-level fusion with rough sets,

T. Zhou, H. Lu, J. Zhang, and H. Shi, “Pulmonary nodule detection model based on svm and ct image feature-level fusion with rough sets,” BioMed research international2016, 8052436 (2016)

2016
[216]

A deep probabilistic sensing and learning model for brain tumor classification with fusion-net and hfcmik segmentation,

M. Ramprasad, M. Z. U. Rahman, and M. D. Bayleyegn, “A deep probabilistic sensing and learning model for brain tumor classification with fusion-net and hfcmik segmentation,” IEEE Open J. Eng. Med. Biol.3, 178–188 (2022)

2022
[217]

Multi-focus image fusion method for vision sensor systems via dictionary learning with guided filter,

Q. Li, X. Yang, W. Wu,et al., “Multi-focus image fusion method for vision sensor systems via dictionary learning with guided filter,” Sensors18, 2143 (2018)

2018
[218]

Brain tumor detection in combined 3d mri and ct images using dictionary learning based segmentation and spearman regression,

V. Anitha, “Brain tumor detection in combined 3d mri and ct images using dictionary learning based segmentation and spearman regression,” Sadhana49, 221 (2024)

2024
[219]

Joint patch clustering-based dictionary learning for multimodal image fusion,

M. Kim, D. K. Han, and H. Ko, “Joint patch clustering-based dictionary learning for multimodal image fusion,” Inf. fusion 27, 198–214 (2016)

2016
[220]

Semisupervised tripled dictionary learning for standard-dose pet image prediction using low-dose pet and multimodal mri,

Y. Wang, G. Ma, L. An,etal., “Semisupervised tripled dictionary learning for standard-dose pet image prediction using low-dose pet and multimodal mri,” IEEE transactions on biomedical engineering64, 569–579 (2016)

2016
[221]

Image fusion based on guided filter and online robust dictionary learning,

J. Li, Y. Peng, M. Song, and L. Liu, “Image fusion based on guided filter and online robust dictionary learning,” Infrared Phys. & Technol.105, 103171 (2020)

2020
[222]

Recent advances in sparse representation based medical image fusion,

Y. Liu, X. Chen, A. Liu,et al., “Recent advances in sparse representation based medical image fusion,” IEEE Instrum. & Meas. Mag.24, 45–53 (2021)

2021
[223]

Medicalimagefusionviaconvolutionalsparsitybasedmorphological component analysis,

Y.Liu,X.Chen,R.K.Ward,andZ.J.Wang,“Medicalimagefusionviaconvolutionalsparsitybasedmorphological component analysis,” IEEE Signal Process. Lett.26, 485–489 (2019)

2019
[224]

A novel region-based multimodal image fusion technique using improved dictionary learning,

B. Meher, S. Agrawal, R. Panda,et al., “A novel region-based multimodal image fusion technique using improved dictionary learning,” Int. J. Imaging Syst. Technol.30, 558–576 (2020)

2020
[225]

A novel multi-focus image fusion method based on stochastic coordinate coding and local density peaks clustering,

Z. Zhu, G. Qi, Y. Chai, and Y. Chen, “A novel multi-focus image fusion method based on stochastic coordinate coding and local density peaks clustering,” Future Internet8, 53 (2016)

2016
[226]

A novel multimodal medical image fusion using sparse representation and modified spatial frequency,

N. Aishwarya and C. Bennila Thangammal, “A novel multimodal medical image fusion using sparse representation and modified spatial frequency,” Int. J. Imaging Syst. Technol.28, 175–185 (2018)

2018
[227]

A new dictionary construction based multimodal medical image fusion framework,

F. Zhou, X. Li, M. Zhou,etal., “A new dictionary construction based multimodal medical image fusion framework,” Entropy 21, 267 (2019)

2019
[228]

A review of multimodal image matching: Methods and applications,

X. Jiang, J. Ma, G. Xiao,etal., “A review of multimodal image matching: Methods and applications,” Inf. Fusion 73, 22–71 (2021)

2021
[229]

Medical image fusion: A survey of the state of the art,

A. P. James and B. V. Dasarathy, “Medical image fusion: A survey of the state of the art,” Inf. fusion19, 4–19 (2014)

2014
[230]

Multimodal medical image fusion review: Theoretical background and recent advances,

H. Hermessi, O. Mourali, and E. Zagrouba, “Multimodal medical image fusion review: Theoretical background and recent advances,” Signal Process.183, 108036 (2021)

2021
[231]

A multi-layer holistic approach for cursive text recognition,

M. Umair, M. Zubair, F. Dawood,etal., “A multi-layer holistic approach for cursive text recognition,” Appl. Sci.12, 12652 (2022)

2022
[232]

Pyramidal image processing using morphology,

G. Eichniann, C. Lu, J. Zhu, and Y. Li, “Pyramidal image processing using morphology,” inApplicationsofDigital ImageProcessing XI, vol. 974 (SPIE, 1988), pp. 30–37

1988
[233]

The morphological pyramid concept as a tool for multi-resolution data fusion in remote sensing,

F. Laporterie and G. Flouzat, “The morphological pyramid concept as a tool for multi-resolution data fusion in remote sensing,” Integr. computer-aided engineering10, 63–79 (2003)

2003
[234]

Ifcnn: A general image fusion framework based on convolutional neural network,

Y. Zhang, Y. Liu, P. Sun,et al., “Ifcnn: A general image fusion framework based on convolutional neural network,” Inf. Fusion54, 99–118 (2020)

2020
[235]

An advanced image fusion algorithm based on wavelet transform: incorporation with pca and morphological processing,

Y. Zheng, E. A. Essock, and B. C. Hansen, “An advanced image fusion algorithm based on wavelet transform: incorporation with pca and morphological processing,” inImageprocessing: algorithms and systemsIII, vol. 5298 (SPIE, 2004), pp. 177–187

2004
[236]

Infrared and visible image fusion using fuzzy logic and population-based optimization,

J. Saeedi and K. Faez, “Infrared and visible image fusion using fuzzy logic and population-based optimization,” Appl. Soft Comput.12, 1041–1054 (2012)

2012
[237]

Multi-focus image fusion algorithm based on multilevel morphological component analysis and support vector machine,

X. Li, L. Wang, J. Wang, and X. Zhang, “Multi-focus image fusion algorithm based on multilevel morphological component analysis and support vector machine,” IET Image Process.11, 919–926 (2017)

2017
[238]

Image fusion using hierarchical pca

U. Patil and U. Mudengudi, “Image fusion using hierarchical pca.” in2011 international conference on image informationprocessing, (IEEE, 2011), pp. 1–6

2011
[239]

Image fusion with morphological component analysis,

Y. Jiang and M. Wang, “Image fusion with morphological component analysis,” Inf. Fusion18, 107–118 (2014)

2014
[240]

Multimodal medical image fusion techniques–a review,

T. Tirupal, B. C. Mohan, and S. S. Kumar, “Multimodal medical image fusion techniques–a review,” Curr. Signal Transduct. Ther.16, 142–163 (2021)

2021
[241]

A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics,

M. A. Azam, K. B. Khan, S. Salahuddin,et al., “A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics,” Comput. biology medicine 144, 105253 (2022)

2022
[242]

Knowledge fusion patterns: A survey,

A. Smirnov and T. Levashova, “Knowledge fusion patterns: A survey,” Inf. Fusion52, 31–40 (2019)

2019
[243]

Knowledge graph based reasoning in medical image analysis: A scoping review,

Q. Huang and G. Li, “Knowledge graph based reasoning in medical image analysis: A scoping review,” Comput. Biol. Med.182, 109100 (2024)

2024
[244]

A brief analysis of multimodal medical image fusion techniques,

M. A. Saleh, A. A. Ali, K. Ahmed, and A. M. Sarhan, “A brief analysis of multimodal medical image fusion techniques,” Electronics12, 97 (2022)

2022
[245]

Multiple attention channels aggregated network for multimodal medical image fusion,

J. Huang, T. Tan, X. Li,et al., “Multiple attention channels aggregated network for multimodal medical image fusion,” Med. Phys
[246]

Multimodal medical image fusion based on interval gradients and convolutional neural networks,

X. Gu, Y. Xia, and J. Zhang, “Multimodal medical image fusion based on interval gradients and convolutional neural networks,” BMC Med. Imaging24, 232 (2024)

2024
[247]

Diagnostictestaccuracyofai-assistedmammographyforbreastimaging: a narrative review,

D.Dave,A.Akhunzada,N.Ivković, etal.,“Diagnostictestaccuracyofai-assistedmammographyforbreastimaging: a narrative review,” PeerJ Comput. Sci.11, e2476 (2025)

2025
[248]

Classification of breast cancer using transfer learning and advanced al-biruni earth radius optimization,

A. A. Alhussan, A. A. Abdelhamid, S. Towfek,etal., “Classification of breast cancer using transfer learning and advanced al-biruni earth radius optimization,” Biomimetics8, 270 (2023)

2023
[249]

Application of cognitive information systems in medical image semantic analysis,

M. R. Ogiela and L. Ogiela, “Application of cognitive information systems in medical image semantic analysis,” Electronics 13, 325 (2024)

2024
[250]

Brain tumor segmentation based on deep learning and an attention mechanism using mri multi-modalities brain images,

R. Ranjbarzadeh, A. Bagherian Kasgari, S. Jafarzadeh Ghoushchi,et al., “Brain tumor segmentation based on deep learning and an attention mechanism using mri multi-modalities brain images,” Sci. reports11, 10930 (2021)

2021
[251]

Glioma brain tumor segmentation in four mri modalities using a convolutional neural network and based on a transfer learning method,

N. Tataei Sarshar, R. Ranjbarzadeh, S. Jafarzadeh Ghoushchi,etal., “Glioma brain tumor segmentation in four mri modalities using a convolutional neural network and based on a transfer learning method,” inBrazilianTechnology Symposium, (Springer, 2021), pp. 386–402

2021
[252]

Brain tumor segmentation of mri images: A comprehensive review on the application of artificial intelligence tools,

R. Ranjbarzadeh, A. Caputo, E. B. Tirkolaee,etal., “Brain tumor segmentation of mri images: A comprehensive review on the application of artificial intelligence tools,” Comput. biology medicine152, 106405 (2023)

2023
[253]

Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm,

R. Ranjbarzadeh, P. Zarbakhsh, A. Caputo,et al., “Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm,” Comput. Biol. Med.168, 107723 (2024)

2024
[254]

Multimodal neuroimage data fusion based on multikernel learning in personalized medicine,

X. Ran, J. Shi, Y. Chen, and K. Jiang, “Multimodal neuroimage data fusion based on multikernel learning in personalized medicine,” Front. Pharmacol.13, 947657 (2022)

2022
[255]

High resolution imaging in bone tissue research-review,

M. Akhter and R. Recker, “High resolution imaging in bone tissue research-review,” Bone143, 115620 (2021)

2021
[256]

Advanced magnetic resonance imaging (mri) of soft tissue tumors: techniques and applications,

F. Bruno, F. Arrigoni, S. Mariani,et al., “Advanced magnetic resonance imaging (mri) of soft tissue tumors: techniques and applications,” La radiologia medica124, 243–252 (2019)

2019
[257]

Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach,

H. A. Owida, G. AlMahadin, J. I. Al-Nabulsi,et al., “Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach,” Int. J. Electr. Comput. Eng.14, 3150–3158 (2024)

2024
[258]

Automated detection of optic disc for the analysis of retina using color fundus image,

M. Zubair, A. Yamin, and S. A. Khan, “Automated detection of optic disc for the analysis of retina using color fundus image,” in2013 IEEE International Conference on Imaging Systemsand Techniques(IST), (IEEE, 2013), pp. 239–242

2013
[259]

Automated segmentation of hard exudates using dynamic thresholding to detect diabetic retinopathy in retinal photographs

M. Zubair, H. Ali, and M. Y. Javed, “Automated segmentation of hard exudates using dynamic thresholding to detect diabetic retinopathy in retinal photographs.” J. Multim. Process. Technol.7, 109–116 (2016)

2016
[260]

Multiclass classification of retinal disorders using optical coherence tomography images,

Z. Shahzadi and M. Zubair, “Multiclass classification of retinal disorders using optical coherence tomography images,” in2024 Horizons of InformationTechnologyandEngineering (HITE), (IEEE, 2024), pp. 1–6

2024
[261]

Autism detection in children by features extraction and classification using a deep learning model,

D. Ahmed, M. A. Hassan, and M. Zubair, “Autism detection in children by features extraction and classification using a deep learning model,” in2024 Horizons of InformationTechnologyand Engineering (HITE), (IEEE, 2024), pp. 1–5

2024
[262]

Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features,

R. Pannu, M. Zubair, M. Owais,et al., “Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features,” Comput. Med. Imaging Graph. p. 102559 (2025)

2025
[263]

Physiologically motivated image fusion for object detection using a pulse coupled neural network,

R. P. Broussard, S. K. Rogers, M. E. Oxley, and G. L. Tarr, “Physiologically motivated image fusion for object detection using a pulse coupled neural network,” IEEE Trans. on Neural networks10, 554–563 (1999)

1999
[264]

Early tumor detection by multiple infrared unsupervised neural nets fusion,

H. Szu, I. Kopriva, P. Hoekstra,et al., “Early tumor detection by multiple infrared unsupervised neural nets fusion,” in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), vol. 2 (IEEE, 2003...

2003
[265]

Medical diagnostic image fusion based on feature mapping wavelet neural networks,

Q. Zhang, M. Liang, and W. Sun, “Medical diagnostic image fusion based on feature mapping wavelet neural networks,” inThird International ConferenceonImageandGraphics (ICIG’04), (IEEE, 2004), pp. 51–54

2004
[266]

Medical image fusion algorithm based on clustering neural network,

X. Lu, B. Zhang, and Y. Gu, “Medical image fusion algorithm based on clustering neural network,” in2007 1st International Conferenceon BioinformaticsandBiomedical Engineering, (IEEE, 2007), pp. 637–640

2007
[267]

Image fusion algorithm using rbf neural networks,

H. Zhang, X.-n. Sun, L. Zhao, and L. Liu, “Image fusion algorithm using rbf neural networks,” inHybrid Artificial IntelligenceSystems: ThirdInternational Workshop,HAIS2008,Burgos,Spain,September24-26,2008. Proceedings 3, (Springer, 2008), pp. 417–424

2008
[268]

Medical image fusion using m-pcnn,

Z. Wang and Y. Ma, “Medical image fusion using m-pcnn,” Inf. fusion9, 176–185 (2008)

2008
[269]

A new fusion model for classification of the lung diseases using genetic algorithm,

C. Bhuvaneswari, P. Aruna, and D. Loganathan, “A new fusion model for classification of the lung diseases using genetic algorithm,” Egypt. Informatics J.15, 69–77 (2014)

2014
[270]

A fusion method for visible and infrared images based on contrast pyramid with teaching learning based optimization,

H. Jin and Y. Wang, “A fusion method for visible and infrared images based on contrast pyramid with teaching learning based optimization,” Infrared Phys. & Technol.64, 134–142 (2014)

2014
[271]

Multimodal medical image fusion based on discrete t chebichef moments and pulse coupled neural network,

L. Tang, J. Qian, L. Li,etal., “Multimodal medical image fusion based on discrete t chebichef moments and pulse coupled neural network,” Int. J. Imaging Syst. Technol.27, 57–65 (2017)

2017
[272]

A survey on deep learning in medical image analysis,

G. Litjens, T. Kooi, B. E. Bejnordi,et al., “A survey on deep learning in medical image analysis,” Med. image analysis 42, 60–88 (2017)

2017
[273]

Deep learning applications in medical image analysis,

J. Ker, L. Wang, J. Rao, and T. Lim, “Deep learning applications in medical image analysis,” Ieee Access6, 9375–9389 (2017)

2017
[274]

A review in wavelet transforms based medical image fusion,

J. Bhardwaj, A. Nayak, C. S. Yadav, and S. P. Yadav, “A review in wavelet transforms based medical image fusion,” Evol. Role AI IoMT Healthc. Mark. pp. 199–214 (2021)

2021
[275]

Multi-level wavelet convolutional neural networks,

P. Liu, H. Zhang, W. Lian, and W. Zuo, “Multi-level wavelet convolutional neural networks,” IEEE Access7, 74973–74985 (2019)

2019
[276]

Analgorithmformulti-sensorimagefusionusingmaximumaposterioriandnonsubsampled contourlet transform,

D.AnandhiandS.Valli,“Analgorithmformulti-sensorimagefusionusingmaximumaposterioriandnonsubsampled contourlet transform,” Comput. & Electr. Eng.65, 139–152 (2018)

2018
[277]

Animagefusionmethodbasedoncurvelettransformandguidedfilterenhancement,

H.Zhang,X.Ma,andY.Tian,“Animagefusionmethodbasedoncurvelettransformandguidedfilterenhancement,” Math. Probl. Eng.2020, 9821715 (2020)

2020
[278]

A novel multi-modal medical image fusion method based on shift-invariant shearlet transform,

L. Wang, B. Li, and L. Tian, “A novel multi-modal medical image fusion method based on shift-invariant shearlet transform,” The Imaging Sci. J.61, 529–540 (2013)

2013
[279]

Improved medical image fusion based on cascaded pca and shift invariant wavelet transforms,

J. Reena Benjamin and T. Jayasree, “Improved medical image fusion based on cascaded pca and shift invariant wavelet transforms,” Int. journal computer assisted radiology surgery13, 229–240 (2018)

2018
[280]

Ontheuseofudwtandfuzzysetsformedicalimagefusion,

T.Tirupal,Y.Pandurangaiah,A.Roy, etal.,“Ontheuseofudwtandfuzzysetsformedicalimagefusion,”Multimed. Tools Appl.83, 39647–39675 (2024)

2024
[281]

Image fusion of ct and mr with sparse representation in nsst domain,

C. Qiu, Y. Wang, H. Zhang, and S. Xia, “Image fusion of ct and mr with sparse representation in nsst domain,” Comput. mathematical methods medicine2017, 9308745 (2017)

2017
[282]

Combination of wavelet and contourlet transforms for pet and mri image fusion,

F. Shabanzade and H. Ghassemian, “Combination of wavelet and contourlet transforms for pet and mri image fusion,” in2017 artificial intelligenceandsignal processingconference(AISP), (IEEE, 2017), pp. 178–183

2017
[283]

Multi-modal medical image fusion using multi-resolution discrete sine transform,

V. Naidu, M. Divya, and P. Mahalakshmi, “Multi-modal medical image fusion using multi-resolution discrete sine transform,” Control. Data Fusion e-Journal1, 13–26 (2017)

2017
[284]

Animagefusionmethodbasedonsparserepresentationandsummodified-laplacian in nsct domain,

Y.Li,Y.Sun,X.Huang, etal.,“Animagefusionmethodbasedonsparserepresentationandsummodified-laplacian in nsct domain,” Entropy20, 522 (2018)

2018
[285]

A fuzzy logic intelligent agent for information extraction: Introducing a new fuzzy logic-based term weighting scheme,

J. Ropero, A. Gómez, A. Carrasco, and C. León, “A fuzzy logic intelligent agent for information extraction: Introducing a new fuzzy logic-based term weighting scheme,” Expert Syst. with Appl.39, 4567–4581 (2012)

2012
[286]

Chaira,Fuzzysetandits extension: Theintuitionisticfuzzyset (John Wiley & Sons, 2019)

T. Chaira,Fuzzysetandits extension: Theintuitionisticfuzzyset (John Wiley & Sons, 2019)

2019
[287]

A framework of fuzzy information fusion for the segmentation of brain tumor tissues on mr images,

W. Dou, S. Ruan, Y. Chen,et al., “A framework of fuzzy information fusion for the segmentation of brain tumor tissues on mr images,” Image vision Comput.25, 164–171 (2007)

2007
[288]

Content analysis based medical images fusion with fuzzy inference,

Y. Na, H. Lu, and Y. Zhang, “Content analysis based medical images fusion with fuzzy inference,” in2008 Fifth International Conferenceon FuzzySystemsandKnowledgeDiscovery, vol. 3 (IEEE, 2008), pp. 37–41

2008
[289]

Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis,

A. Assareh and L. G. Volkert, “Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis,” in2009IEEESymposiumonComputationalIntelligenceinBioinformaticsandComputationalBiology, (IEEE, 2009), pp. 193–199

2009
[290]

Neuro-fuzzy logic based fusion algorithm of medical images,

J. Teng, S. Wang, J. Zhang, and X. Wang, “Neuro-fuzzy logic based fusion algorithm of medical images,” in2010 3rd International Congress on Imageand SignalProcessing, vol. 4 (IEEE, 2010), pp. 1552–1556

2010
[291]

A neuro-fuzzy approach for medical image fusion,

S. Das and M. K. Kundu, “A neuro-fuzzy approach for medical image fusion,” IEEE transactions on biomedical engineering 60, 3347–3353 (2013)

2013
[292]

Multimodal medical image fusion based on type-1 fuzzy sets,

K. Gayathri and T. Tirupal, “Multimodal medical image fusion based on type-1 fuzzy sets,” J Appl Sci Comput.5, 1329–1341 (2018)

2018
[293]

Image fusion using intuitionistic fuzzy sets,

P. Balasubramaniam and V. Ananthi, “Image fusion using intuitionistic fuzzy sets,” Inf. fusion20, 21–30 (2014)

2014
[294]

Multimodal medical image fusion based on undecimated wavelet transform and fuzzy sets,

P. K. Peruru, K. Madhavi, and T. Tirupal, “Multimodal medical image fusion based on undecimated wavelet transform and fuzzy sets,” Int. J. Innov. Technol. Explor. Eng.8, 7–103 (2019)

2019
[295]

An effective fusion model of modified wavelet transform for medical diagnosis by using wireless optical communications,

D. Poornima, S. Gowri, J. Jabezet al., “An effective fusion model of modified wavelet transform for medical diagnosis by using wireless optical communications,” in2023 International Conferenceon Emerging Researchin Computational Science (ICERCS), (IEEE, 2023), pp. 1–6

2023
[296]

Multi-focus image fusion based on sparse representation with adaptive sparse domain selection,

Y. Liu and Z. Wang, “Multi-focus image fusion based on sparse representation with adaptive sparse domain selection,” in2013SeventhInternational ConferenceonImageandGraphics, (IEEE, 2013), pp. 591–596

2013
[297]

Ddcgan: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion,

J. Ma, H. Xu, J. Jiang,et al., “Ddcgan: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion,” IEEE Trans. on Image Process.29, 4980–4995 (2020)

2020
[298]

3d brain tumor segmentation in mri using fuzzy classification, symmetry analysis and spatially constrained deformable models,

H. Khotanlou, O. Colliot, J. Atif, and I. Bloch, “3d brain tumor segmentation in mri using fuzzy classification, symmetry analysis and spatially constrained deformable models,” Fuzzy sets systems160, 1457–1473 (2009)

2009
[299]

Multimodal brain network jointly construction and fusion for diagnosis of epilepsy,

Q. Zhu, J. Yang, B. Xu,et al., “Multimodal brain network jointly construction and fusion for diagnosis of epilepsy,” Front. Neurosci.15, 734711 (2021)

2021
[300]

Multimodality data integration in epilepsy,

O. Muzik, D. C. Chugani, G. Zou,etal., “Multimodality data integration in epilepsy,” Int. J. Biomed. Imaging2007, 013963 (2007)

2007

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Medicalimagingmodalities,

H.A.Ahmad, H.J.Yu, andC.G.Miller, “Medicalimagingmodalities,” Med.imagingclinicaltrialspp.3–26(2014)

2014

[2] [2]

Introduction of medical imaging modalities,

S. M. S. Islam, M. A. A. Nasim, I. Hossain,et al., “Introduction of medical imaging modalities,” inData Driven ApproachesonMedical Imaging, (Springer, 2023), pp. 1–25

2023

[3] [3]

Review of multimodality medical image fusion using combined transform techniques for clinical application,

B. Rajalingam, R. Priya, and R. Scholar, “Review of multimodality medical image fusion using combined transform techniques for clinical application,” Int. J. Sci. Res. Comput. Sci. Appl. Manag. Stud.7, 1–8 (2018)

2018

[4] [4]

Is ultrasound safe?

J. Joy, I. Cooke, and M. Love, “Is ultrasound safe?” The Obstet. & Gynaecol.8, 222–227 (2006)

2006

[5] [5]

Ultrasound imaging and contrast agents: a safe alternative to mri?

M. H. Wink, H. Wijkstra, J. J. De La Rosette, and C. A. Grimbergen, “Ultrasound imaging and contrast agents: a safe alternative to mri?” Minim. Invasive Ther. & Allied Technol.15, 93–100 (2006)

2006

[6] [6]

Role of multimodality imaging in the diagnosis and management of cardiomyopathies,

S. Ederhy, N. Mansencal, P. Réant,et al., “Role of multimodality imaging in the diagnosis and management of cardiomyopathies,” Arch. cardiovascular diseases112, 615–629 (2019)

2019

[7] [7]

Application of ai in cardiovascular multimodality imaging,

G. Muscogiuri, V. Volpato, R. Cau,etal., “Application of ai in cardiovascular multimodality imaging,” Heliyon8 (2022)

2022

[8] [8]

Contributionandperformanceofmultimodalimaginginthediagnosis and management of cardiac masses,

M.Lemasle,Y.LavieBadie,E.Cariou, etal.,“Contributionandperformanceofmultimodalimaginginthediagnosis and management of cardiac masses,” The Int. J. Cardiovasc. Imaging36, 971–981 (2020)

2020