This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Mahdi Koohi , Behzad Moshiri, Abbas Shakery
In this modern era, medical image processing is an indispensable part of many applications and practices in the medical domain. The images that are used should meet certain criteria, including having more accurate details and information than each individual image, which can help medical scientists with analysis and treatment. Medical image fusion is among the techniques that offer high-quality images, which are combined from different modalities. Multimodal medical image fusion provides remarkable improvement in the quality of the fused images. In this paper, we describe an image fusion method for magnetic resonance imaging (MRI) and computed tomography (CT) utilizing local features and fuzzy logic methods. The aim of the proposed technique is to create the maximum combination of useful information present in MRI and CT images. Image local features are distinguished and combined with fuzzy logic to calculate weights for each pixel. Simulation outcomes show that the proposed method produces considerably better results compared to cutting-edge techniques. The method is also used to detect and highlight tumorous areas, followed by morphology filters used to eliminate any noise and disturbance.
 Er. Anjna, Er.Rajandeep Kaur, “Review of Image Segmentation Technique” Volume 8, No. 4, May 2017 (Special Issue)
 Abbas shakeri,Behadmoshiri, hosseingharaee, “Pedestrian Detection using Image Fusion and stereo vision in autonomous vehicles” 2018 9th International Symposium on Telecommunications (IST'2018)
 Kumar, Mahendra. (2018). Image fusion based on evolutionary optimization algorithm. 10.13140/RG.2.2.13146.59845.
 Pradeep K. Atrey, and M. Anwar Hossain, “Multimodal Fusion for Multimedia Analysis: A Survey”, Multimedia Systems, DOI:10.1007/s00530-010-0182-0, Springer Verlag, 2010.
 A.P. James, and B.V. Dasarathy, “Medical Image Fusion: A survey of the State of the Art”, Information Fusion, vol. 19, pp.4-19, 2014.
 Fatma E. El-Gamal, and Mohammed Elmogy, “Current Trends in Medical Image Registration and Fusion”, Egyptian Informatics Journal, 2015.
 K.P.Indira, and R.RaniHemamalini, “Analysis on Image Fusion Techniques for Medical Applications”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, Issue 9, 2014.
 Harmandeep Kaur, Er. Jyoti Rani, “Analytical Comparison of Various Image Fusion Techniques” International Journal of Advanced
 Jasmeetkaur, Er. Rajdavinder, “An Evaluation on Different Image Fusion Techniques”, IPASJ International Journal of Computer Science (IIJCS), vol. 2, Issue 4, 2014.
 Fatma El-Zahraa Ahmed El-Gamal, Mohammed Elmogy *,Ahmed Atwan “Current trends in medical image registration and fusion “Egypt journal August 2015 Volume 17,pp,99 -124
 Rudra Pratap Singh Chauhan,Rajiva Dwivedi and Sandeep Negi “ Comparative Evaluation of DWT and DT-CWT for Image Fusion and De-noising”, International Journal of Applied Information Systems (IJAIS),Volume 4– No.2, September 2012 – ISSN : 2249-0868.
 Zhang, H., Fritts, J. E., & Goldman, S. A. (2008). Image segmentation evaluation: A survey of unsupervised methods. Comput Vis Image Underst 110(2), 260–280.
 Mahdi Koohi,Abbasshakeri,Mehdi Naraghi, “Edge detection in multispectral images based on structural ”, Internationa journal of multimedia & its application Vol.3, No.1 , Feb. 2011.
 Runkler, T.A., Katz, C.: Fuzzy clustering by particle swarm optimization. In: Proceedings of2006 IEEE International Conference on Fuzzy Systems, pp. 601–608. Canada (2006).
 Huang, M., Xia, Z., Wang, H., Zeng, Q., Wang, Q.: The range of the value for the fuzzifier ofthe fuzzy c-means algorithm. Pattern Recogn. Lett. 33, 2280–2284 (2012).
 B. Rajalingama, R. Priya b , R.Bhavanic “Hybrid Multimodal Medical Image Fusion Using Combination of Transform Techniques for Disease Analysis” International Conference on Pervasive Computing Advances and Applications – PerCAA 2019.
 T. Tirupal1, B. Chandra Mohan , S. Srinivas Kumar,” Multimodal medical image fusion based on yager’s intuitionistic fuzzy sets.” Iranian Journal of Fuzzy Systems, Volume 16, Number 1, (2019), pp. 33-48
 Bing Huang,Feng Yang , Mengxiao Yin, Xiaoying Mo, Cheng Zhong.” A Review of Multimodal Medical Image Fusion Techniques”. Computational and Mathematical Methods in MedicineVolume 2020, Article ID 8279342, 16 pages.
 Munish Rehal , Akhil Goyal,” Multimodal Image Fusion based on Hybrid of Hilbert Transform and Intensity Hue Saturation using Fuzzy System”. International Journal of Computer Applications (0975 – 8887)Volume 183– No.4, May 2021.
Department of Electronic Engineering, University College of Engineering, Tehran, Iran
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