Medical Image Fusion Based on Sparse Representation and PCNN in NSCT Domain

Te clinical assistant diagnosis has a high requirement for the visual efect of medical images. However, the low frequency subband coefcients obtained by the NSCT decomposition are not sparse, which is not conducive to maintaining the details of the source image. To solve these problems, a medical image fusion algorithm combined with sparse representation and pulse coupling neural network is proposed. First, the source image is decomposed into low and high frequency subband coefcients by NSCT transform. Secondly, the K singular value decomposition (K-SVD) method is used to train the low frequency subband coefcients to get the overcomplete dictionary �, and the orthogonal matching pursuit (OMP) algorithm is used to sparse the low frequency subband coefcients to complete the fusion of the low frequency subband sparse coefcients. Ten, the pulse coupling neural network (PCNN) is excited by the spatial frequency of the high frequency subband coefcients, and the fusion coefcients of the high frequency subband coefcients are selected according to the number of ignition times. Finally, the fusion medical image is reconstructed by NSCT inverter. Te experimental results and analysis show that the algorithm of gray and color image fusion is about 34% and 10% higher than the contrast algorithm in the edge information transfer factor QAB/F index, and the performance of the fusion result is better than the existing algorithm.