Medical Image Fusion Combining Sparse Representation and Neural Network
Journal Title: 河南科技大学学报（自然科学版） - Year 2018, Vol 39, Issue 2
Abstract
The clinical auxiliary diagnosis needs a higher requirement for the visual effects of medical images, but the low frequency subband coefficients obtained by the non-subsampled contourlet transform ( NSCT) decomposition were not sparse and not conducive to maintain the details of the source image. So a medical image fusion algorithm combining sparse representation and pulse coupled neural network ( PCNN) was proposed.Firstly, the original image was decomposited by non-subsampled contourlet transform ( NSCT) to obtain low and high frequency subbands ceefficients. Secondly, the K singular value decomposition ( K-SVD) method was used to train the low frequency subband coefficients to obtain the over-complete dictionary matrix D. The orthogonal matching pursuit ( OMP) algorithm was used to encode the low frequency subband coefficients, which achieved the fusion of sparse coefficients of the low frequency subband. Then, the spatial frequency of high frequency subband was used to excitate PCNN.The coefficient of the larger ignition frequency was selected as the fusion coefficient of high frequency subband. Finally, the NSCT inverse transform was applied to low and high frequency subband fusion coefficients to obtain the fused medical image. The experimental results show that the gray and color image fusion results of the proposed algorithm rise by 34%and 10% than the contrast algorithm in the edge information transfer factor QAB/F index. The comprehensive performance is superior to the existing algorithm.
Authors and Affiliations
Yiming CHEN, Jingming XIA, Yicai CHEN, Gang ZHOU
Keywords
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