Abstract
The clinical assistant diagnosis has a high requirement for the visual effect of medical images. However, the low frequency subband coefficients 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 coefficients by NSCT transform. Secondly, the K singular value decomposition (K-SVD) method is used to train the low frequency subband coefficients to get the overcomplete dictionary D, and the orthogonal matching pursuit (OMP) algorithm is used to sparse the low frequency subband coefficients to complete the fusion of the low frequency subband sparse coefficients. Then, the pulse coupling neural network (PCNN) is excited by the spatial frequency of the high frequency subband coefficients, and the fusion coefficients of the high frequency subband coefficients are selected according to the number of ignition times. Finally, the fusion medical image is reconstructed by NSCT inverter. The 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.
Highlights
Medical imaging attracts more and more attention due to the increasing requirements of clinic investigation and disease diagnosis [1]
NSCT-Sparse Representation (SR)-pulse coupling neural network (PCNN) algorithm firstly uses NSCT transform to decompose the source image after registration to obtain the low frequency and high frequency subband of the source image; secondly the fusion method based on sparse representation is used to fuse the low frequency subband, and the fusion method based on PCNN simplified model is used to fuse the high frequency subband; NSCT inverse transform is used to reconstruct the fused subband coefficients to obtain the medical image of fusion
For fusion based on PCNN simplified model, spatial frequency (SF) is used as the neuron feedback input to excite each neuron, and EOL, VI, and standard deviation (SD) are selected as the linking strength values of the corresponding neurons; the corresponding ignition map is obtained by the PCNN ignition, and the new ignition map of the source image is constructed by the weighting function
Summary
Medical imaging attracts more and more attention due to the increasing requirements of clinic investigation and disease diagnosis [1]. The PCNN model has global coupling and pulse synchronization, which can combine the input high frequency subband coefficients with human visual characteristics to obtain richer detail information [6]. Mohammed et al [10] propose a medical image fusion algorithm based on sparse representation and dual input PCNN model This algorithm has a high fusion performance and adapts to the human visual nerve system. It needs to train medical image database to get an overcomplete dictionary; in addition, PCNN model applies a dual input, which presents the high complexity and low integration efficiency of the algorithm. In order to obtain the medical fusion image with high fusion performance and high fusion efficiency, and to help it adapt to human visual nervous system, this paper, by aiming at the above research situation and existing problems and combining the sparse representation with PCNN simplified model, proposes the medical image fusion algorithm based on NSCT and SR-PCNN, hereinafter referred to as NSCT-SRPCNN fusion algorithm
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