Multimodal Medical Image Fusion Using Various Hybrid Fusion Techniques For clinical Treatment Analysis
DOI:
https://doi.org/10.18063/scr.v2i4.594Keywords:
Multimodal medical image fusion, MRI, PET, SPECT, PCA, DCT, DWT, DCHWT, GIF, curvelet transform, Sub-band Decomposition, Ridgelet Transform, PCNN, Neuro-FuzzyAbstract
Medical image fusion is one the most significant and useful disease analytic techniques. This research paper proposed and examines some of the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods to develop hybrid multimodal image fusion algorithms that improve the feature of merged multimodality therapeutic image. Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography and Single Photon Emission Computed Tomography are the input multimodal therapeutic images used for fusion process. An experimental results of proposed all hybrid fusion techniques provides the best fused multimodal medical images of highest quality, highest details, shortest processing time, and best visualization. Both traditional and hybrid multimodal medical image fusion algorithms are evaluated using several quality metrics. Compared with other existing techniques the proposed technique experimental results demonstrate the better processing performance and results in both subjective and objective evaluation criteria. This is favorable, especially for helping in accurate clinical disease analysis.
References
B.Rajalingam, Dr. R.Priya, “Multimodality Medical Image Fusion Based on Hybrid Fusion Techniques” International Journal of Engineering and Manufacturing Science. Volume 7, No. 1, 2017, pp. 22-29
B.Rajalingam, Dr. R.Priya, “A Novel approach for Multimodal Medical Image Fusion using Hybrid Fusion Algorithms for Disease Analysis” International Journal of Pure and Applied Mathematics, Volume 117, No. 15, 2017, pp. 599-619
B.Rajalingam, Dr. R.Priya, “Hybrid Multimodality Medical Image Fusion Technique for Feature Enhancement in Medical Diagnosis” International Journal of Engineering Science Invention (IJESI), Volume 2, Special issue, 2018, pp. 52-60
Srinivasa Rao D, Seetha M, Krishna Prasad MHM “Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications” International Journal of Computer Applications Volume 43, No.20, 2012, pages. 31 - 37
Saad M. Darwish “Multi-level fuzzy contourlet-based image fusion for medical applications” IET Image Process, Volume 7, Issue. 7, 2013, pp. 694-700
Sudeb Das, Malay Kumar Kundu “A Neuro-Fuzzy Approach for Medical Image Fusion” IEEE Transactions on Biomedical Engineering , Volume 60, Issue.12, 2013, pp. 3347 - 3353
C. T. Kavitha, C.Chellamuthu “Multimodal Medical Image Fusion Based on Integer Wavelet Transform and Neuro-Fuzzy” International Conference on Signal and Image Processing, IEEE, 2010
Meenu Manchanda, Rajiv Sharma “A novel method of multimodal medical image fusion using fuzzy transform” Journal of Visual Communication and Image Representation, Volume 40, Part A, 2016, pp. 197-217
Yong Yang, Yue Que, Shuying Huang, and Pan Lin “Multimodal Sensor Medical Image Fusion Based on Type-2 Fuzzy Logic in NSCT Domain” IEEE Sensors Journal, Volume 16, Issue. 10, 2016, pp. 3735 - 3745
Jiao Du, Weisheng Li, Ke Lu, Bin Xiao “An Overview of Multi-Modal Medical Image Fusion” Elsevier, Neurocomputing, Volume 215, 2016, pp. 3-20
Jiao Du, WeishengLi n, BinXiao, QamarNawaz “Union Laplacian pyramid with multiple features for medical image fusion” Elsevier, Neurocomputing, Volume 194, 2016, pp. 326-339
xingbin Liu, Wenbo Mei, Huiqian Du “Structure tensor and nonsubsampled sheasrlet transform based algorithm for CT and MRI image fusion” Elsevier, Neurocomputing, Volume 235, 2017, pp. 131-139
K.N. Narasimha Murthy and J. Kusuma “Fusion of Medical Image Using STSVD” Springer, Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, volume 516, 2017, pp 69-79
Satishkumar S. Chavana, Abhishek Mahajanb, Sanjay N. Talbarc, Subhash Desaib, Meenakshi Thakurb, Anil D'cruzb “Nonsubsampled rotated complex wavelet transform (NSRCxWT) for medical image fusion related to clinical aspects in neurocysticercosis” Elsevier, Computers in Biology and Medicine, No. 81, 2017, pp. 64–78
S. Chavan, A. Pawar and S. Talbar “Multimodality Medical Image Fusion using Rotated Wavelet Transform” Advances in Intelligent Systems Research, Vol. 137, 2017, pp. 627-635
Heba M. El-Hoseny, El-Sayed M. El.Rabaie, Wael Abd Elrahman, and Fathi E Abd El-Samie “Medical Image Fusion Techniques Based on Combined Discrete Transform Domains” 34th National Radio Science Conference, IEEE , 2017
Udhaya Suriya TS, Rangarajan P “Brain tumour detection using discrete wavelet transform based medical image fusion” Biomedical Research, 28 (2), 2017, pp. 684-688
Periyavattam Shanmugam Gomathi, Bhuvanesh Kalaavathi “Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain” Scientific Research Publishing, Circuits and Systems, No.7, 2016, pp. 1598-1610
C.Karthikeyan and B. Ramadoss “Comparative Analysis of Similarity Measure Performance for Multimodality Image Fusion using DTCWT and SOFM with Various Medical Image Fusion Techniques” Indian Journal of Science and Technology, Volume 9, Issue 22, 2016
Xinzheng Xua, Dong Shana, Guanying Wanga, Xiangying “Multimodal medical image fusion using PCNN optimized by the QPSO algorithm” Elsevier, Applied Soft Computing, 2016
Jyoti Agarwal and Sarabjeet Singh Bedi “Implementation of hybrid image fusion technique for feature enhancement in medical diagnosis” Springer, Human-centric Computing and Information Sciences, 2015
Jing-jing Zonga, Tian-shuang Qiua, “Medical image fusion based on sparse representation of classified image patches” Elsevier, Biomedical Signal Processing and Control, No. 34, 2017 pp. 195–205
Richa Gautam and Shilpa Datar “Application of image fusion techniques on medical images” International Journal of Current Engineering and Technology, Volume 7, No.1, 2017, pp. 161-167
Xiaojun Xu, Youren Wang, Shuai Chen “Medical image fusion using discrete fractional wavelet transform” Elsevier, Biomedical Signal Processing and Control, No. 27, 2016, pp. 103–111
Zhaobin Wang, Shuai Wang,Ying Zhu, Yide Ma “Review of image fusion based on pulse-coupled neural network” Springer, Archives of Computational Methods in Engineering, Volume 23, Issue 4, 2016, pp. 659–671
Shutao Li, Xudong Kang S And Jianwen Hu “Image fusion with guided filtering” Transactions on Image Processing, IEEE Transactions on Image Processing, Volume: 22, Issue 7, 2013, pp. 2864 - 2875
