A Multimodal Image Fusion based on NonSubsampled Contourlet Transform and Sparse Representation
Detection of tumors in the brain is vital in diagnosis of brain cancer. Doctors suggest numerous scans like CT, MRI, PET, and SPECT for estimating the type of cancer, size and location of the tumor and the aging or spread of cancer. A single imaging technique is not sufficient for correct diagnosis of the disease. In case the scans are ambiguous, it can lead doctors to incorrect diagnosis, which can be unsafe to the patient. The solution to this problem is fusing images from different scans containing complementary information to generate accurate images with minimum uncertainty. There are many ways of fusing images; the techniques considered in this paper are based on Multiscale transforms and Sparse Representation. By using, these 2 techniques a novel image fusion algorithm is proposed. NSCT and NSCT-SR are implemented and results are reported on 12 pairs of CT and MRI images. The experimental results show superior performance in terms of contrast, PSNR, UIQI and SSIM for the proposed technique.
Wolf-Dieter Hies, Peter Raab and Heinrich Lanferman 2011, Multimodality Assessment of Brain Tumors and Tumor Recurrence, The Journal of Nuclear Medicine J Nucl Med.2011;52:1585-1600.
Jiao Du, Weisheng Li, Ke Lu, Bin Xiao, A overview of multi-modal medical image fusion, Neurocomputing 215 (2016) 3-20.
A P James, B V Dasarathy, Medical Image Fusion: A survey of the state of art, Information Fusion, 2014.
Seiichi Serikawa, Huimin Lu, Yujie Li et al, Multi modal medical Image Fusion in Extended Contourlet Transform Domain, R.Lee (Ed): Software Engineering, Artifical Intelligence, Networking and Parallel/Distributed Computing 2012, pp.215-226.
H Li, B S Manjunath and S K Mitra,” Multisensor image fusion using the wavelet transform”, Graphical Models and Image Processing, Vol 57, Issue 3, May 1995, Pages 235-245.
V.P.S Naidu and J R Raol, “Pixel-level Image Fusion using Wavelets and Principal Component Analysis”, Defence Science Journal, Vol. 58, No.3, May 2008, pp.338-352.
S M Mahbubur Rahman, M Omair Amad, M N S Swamy, “ Contrast-based fusion of noisy images using Discrete Wavelet Transform,” IET Image Processing, 2010,Vol.4,Iss.5,pp.374-384.
Peter J Burt and Edward H Adelson, “ The Laplacian Pyramid as a Compact Image Code,” IEEE Transactions on Communications,Vol. COM-31,No.4, April 1983.
Hang Tan, Xianhe Huang, Huachan Tan, Changtao He, “Pixel-like Image Fusion Algorithm based on Maximum Likelihood and Laplacian Pyramid Transformation”, Journal of Computational Information Systems 9:1(2013) 327-334.
Akanksha Sahu, Vikrant Bhateja, Abhinav Krishna and Himanshi, “Medical Image Fusion with Laplacian Pyramids”, 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom).
Minh N Do and Martin Vetterli, “ The Contourlet Transform: An Efficient Dirrectional Multiresolution Image Representation,” IEEE Transaction of Image Processing, Vol.14,2005.
Arthur L da Cunha, Jianping Zhou, Minh N Do, “ The Nonsunsampled Contourlet Transform: Theory, Design and Applications,” IEEE Transactions on Image Processing, Vol.15, No.10, October 2006.
Huafeng Li, Yi Chai and Zhaofei Li, Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection,” Optik 124 (2013) 40-51.
Gaurav Bhatnagar, Q M Jonathan Wu and Zheng Liu,“Directive Contrast Based Multimodal Medical Image fusion in NSCT Domain.” IEEE Transactions on Multimedia, Vol. 15, No.5, August 2013.
Jun Wang, Jinye Peng et al, “Image fusion with nonsubsampled contourlet transform and sparse representation,”Journal of Electronic Imaging 22(4), 043019, Oct –Dec 2013.
Yu Liu, Shuping Liu and Zengfu Wang, “A general framework for image fusion based on multi scale transform and sparse representation,” Information Fusion 24 (2015) 147-164.
Bin Yang and Shutao Li, Multifocus Image Fusion and Restoration With Sparse Representation, IEEE Transactiona on Instrumentation and Measurement, Vol. 59, No.4, April 2010.
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