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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background:

Modern medical imaging modalities used by clinicians have many applications in the diagnosis of complicated diseases. These imaging technologies reveal the internal anatomy and physiology of the body. The fundamental idea behind medical image fusion is to increase the image's global and local contrast, enhance the visual impact, and change its format so that it is better suited for computer processing or human viewing while preventing noise magnification and accomplishing excellent real-time performance.

Objective:

The top goal is to combine data from various modal images (CT/MRI and MR-T1/MR-T2) into a solitary image that, to the greatest degree possible, retains the key characteristics (prominent features) of the source images.

Methods:

The clinical accuracy of medical issues is compromised because innumerable classical fusion methods struggle to conserve all the prominent features of the original images. Furthermore, complex implementation, high computation time, and more memory requirements are key problems of transform domain methods. With the purpose of solving these problems, this research suggests a fusion framework for multimodal medical images that makes use of a multi-scale edge-preserving filter and visual saliency detection. The source images are decomposed using a two-scale edge-preserving filter into base and detail layers. Base layers are combined using the addition fusion rule, while detail layers are fused using weight maps constructed using the maximum symmetric surround saliency detection algorithm.

Results:

The resultant image constructed by the presumed method has improved objective evaluation metrics than other classical methods, as well as unhindered edge contour, more global contrast, and no ringing effect or artifacts.

Conclusion:

The methodology offers a dominant and symbiotic arsenal of clinical symptomatic, therapeutic, and biomedical research competencies that have the prospective to considerably strengthen medical practice and biological understanding.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2023-10-16
2025-09-09

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References

  1. JamesA.P. DasarathyB.V. Medical image fusion: A survey of the state of the art.Inf. Fusion20141941910.1016/j.inffus.2013.12.002
     [Google Scholar]
  2. KoundalD GuptaS SinghS Computer aided thyroid nodule detection system using medical ultrasound images.Biomed Signal Proces20184011713010.1016/j.bspc.2017.08.025
     [Google Scholar]
  3. KaushalC. BhatS. KoundalD. SinglaA. Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images.IRBM201940421122710.1016/j.irbm.2019.06.001
     [Google Scholar]
  4. El-GamalF.E-Z.A. ElmogyM. AtwanA. AtwanA. Current trends in medical image registration and fusion.Egypt. Inform. J20161719912410.1016/j.eij.2015.09.002
     [Google Scholar]
  5. HermessiH. MouraliO. ZagroubaE. Multimodal medical image fusion review: Theoretical background and recent advances.Signal Process.202118310803610.1016/j.sigpro.2021.108036
     [Google Scholar]
  6. LiS. KangX. FangL. HuJ. YinH. Pixel-level image fusion: A survey of the state of the art.Inf. Fusion20173310011210.1016/j.inffus.2016.05.004
     [Google Scholar]
  7. TirupalT. MohanB.C. KumarS.S. Multimodal medical image fusion techniques. A review.Curr. Signal Transduct. Ther.202116214216310.2174/1574362415666200226103116
     [Google Scholar]
  8. TawfikN. ElnemrH.A. FakhrM. DessoukyM.I. Abd El-SamieF.E. Survey study of multimodality medical image fusion methods.Multimedia Tools Appl.20218046369639610.1007/s11042‑020‑08834‑5
     [Google Scholar]
  9. ShreyamshaB.K. Image fusion based on pixel significance using cross bilateral filter.SIViP20131193120410.1007/s11760‑013‑0556‑9
     [Google Scholar]
  10. BavirisettiD.P. DhuliR. Two-scale image fusion of visible and infrared images using saliency detection.Infrared Phys. Technol.201676526410.1016/j.infrared.2016.01.009
     [Google Scholar]
  11. MaJ. ZhouZ. WangB. ZongH. Infrared and visible image fusion based on visual saliency map and weighted least square optimization.Infrared Phys. Technol.20178281710.1016/j.infrared.2017.02.005
     [Google Scholar]
  12. ZhangY. GuoC. ZhaoP. Medical Image Fusion Based on Low-Level Features.Comput. Math. Methods Med.2021202111310.1155/2021/879800334221107
     [Google Scholar]
  13. LiuS. HuangS. WangS. MuhammadK. BellavistaP. Del SerJ. Visual tracking in complex scenes: A location fusion mechanism based on the combination of multiple visual cognition flows.Inf. Fusion20239628129610.1016/j.inffus.2023.02.005
     [Google Scholar]
  14. LiuS. GaoP. LiY. FuW. DingW. Multi-modal fusion network with complementarity and importance for emotion recognition.Inf. Sci.202361967969410.1016/j.ins.2022.11.076
     [Google Scholar]
  15. BorjiA. IttiL. State-of-the-art in visual attention modeling.IEEE Trans. Pattern Anal. Mach. Intell.201335118520710.1109/TPAMI.2012.8922487985
     [Google Scholar]
  16. WenG. Rodriguez-NiñoB. PecenF.Y. ViningD.J. GargN. MarkeyM.K. Comparative study of computational visual attention models on two-dimensional medical images.J. Med. Imaging (Bellingham)20174202550310.1117/1.JMI.4.2.02550328523282
     [Google Scholar]
  17. AchantaR. S¨usstrunkS. Saliency detection using maximum symmetric surround.IEEE International Conference on Image ProcessingHong Kong, China, 2010, pp. 2653-2656.,10.1109/ICIP.2010.5652636
     [Google Scholar]
  18. WangX.H. IstepanianR.S.H. SongY.H. Yong Hua Song Microarray image enhancement by denoising using stationary wavelet transform.IEEE Trans. Nanobiosci.20032418418910.1109/TNB.2003.81622515376907
     [Google Scholar]
  19. Shutao Li Xudong Kang Jianwen Hu Image fusion with guided filtering.IEEE Trans. Image Process.20132272864287510.1109/TIP.2013.224422223372084
     [Google Scholar]
  20. LiuZ. BlaschE. XueZ. ZhaoJ. LaganiereR. WuW. Objective assessment of multi resolution image fusion algorithms for context enhancement in night vision: A comparative study.IEEE Trans. Pattern Anal. Mach. Intell.20123419410910.1109/TPAMI.2011.10921576753
     [Google Scholar]
  21. WangZ. BovikA.C. SheikhH.R. SimoncelliE.P. Image quality assessment: From error visibility to structural similarity.IEEE Trans. Image Process.200413460061210.1109/TIP.2003.81986115376593
     [Google Scholar]
  22. KaurH. VigR. KumarN. DograA. SharmaA. GoyalB. Objective image quality assessment of pixel level image fusion algorithms for medical imaging.IEEE Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)Trichirappalli, India, 2023, pp. 01-08.10.1109/ICEEICT56924.2023.10157703
     [Google Scholar]
  23. SunJ. HanQ. KouL. ZhangL. ZhangK. JinZ. Multi-focus image fusion algorithm based on Laplacian pyramids.J. Opt. Soc. Am. A Opt. Image Sci. Vis.201835348049010.1364/JOSAA.35.00048029522052
     [Google Scholar]
  24. PajaresG. Manuel de la CruzJ. A wavelet-based image fusion tutorial.Pattern Recognit.20043791855187210.1016/j.patcog.2004.03.010
     [Google Scholar]
  25. YangY. ParkD.S. HuangS. RaoN. Medical image fusion via an effective wavelet-based approach.EURASIP J. Adv. Signal Process.20102010157934110.1155/2010/579341
     [Google Scholar]
  26. KingsburyN. Image processing with complex wavelets.Philos. Trans.- Royal Soc., Math. Phys. Eng. Sci.199935717602543256010.1098/rsta.1999.0447
     [Google Scholar]
  27. NaiduV.P.S. DT CWT based image fusion.2023Available From: https://www.mathworks.com/matlabcentral/fileexchange/32086-dt-cwt-based-image-fusion
  28. SrivastavaR. PrakashO. KhareA. Local energy‐based multimodal medical image fusion in curvelet domain.IET Comput. Vis.201610651352710.1049/iet‑cvi.2015.0251
     [Google Scholar]
  29. BavirisettiD.P. KolluV. GangX. DhuliR. Fusion of MRI and CT images using guided image filter and image statistics.Int. J. Imaging Syst. Technol.201727322723710.1002/ima.22228
     [Google Scholar]
  30. Available From: https://github.com/biswajitcsecu/Guided-Bilateral-Filter-based-Medical-Image-Fusion-Using-Visual-
  31. LiuY. WangZ. Simultaneous image fusion and denoising with adaptive sparse representation.IET Image Process.20159534735710.1049/iet‑ipr.2014.0311
     [Google Scholar]
  32. ZhuR LiX ZhangX MaM. MRI and CT medical image fusion based on synchronized-anisotropic diffusion model.IEEE Access8913369135010.1109/ACCESS.2020.2993493
     [Google Scholar]
  33. TanW. TiwariP. PandeyH.M. MoreiraC. JaiswalA.K. Multimodal medical image fusion algorithm in the era of big data.Neural Comput. Appl.202010.1007/s00521‑020‑05173‑2
     [Google Scholar]
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Multimodal Medical Image Fusion Utilizing Two-scale Image Decomposition via Saliency Detection
Curr. Med. Imaging 20, e15734056260083 (2024); https://doi.org/10.2174/0115734056260083230924154700
/content/journals/cmir/10.2174/0115734056260083230924154700
/content/journals/cmir/10.2174/0115734056260083230924154700
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  • Article Type:     Research Article
Keyword(s): CT\MRI; Medical practice; Multi-modal image fusion; Multi-scale decomposition; Research precision; Saliency detection

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