Abstract A novel fusion algorithm for medical image based on sparse representation and pulse coupled neural network (PCNN) was proposed to meet the demand of computeraided diagnosis from the medical images. First, the K-SVD algorithm was used to obtain the redundant dictionary of the joint matrix which was obtained by sliding window technique. Next, sparse coefficients for the joint matrix were set up through orthogonal matching pursuit (OMP) algorithm. Then, the sparse coefficients were fused by a PCNN based on their characteristics. At last, the fused image was obtained by transforming the fused matrix which was got by the fused sparse coefficients and redundant dictionary. Ten groups of coaligned medical images were tested by experiments and the quality of the fused image was evaluated by five kinds of commonly used objective criterions. Comparing with the other two popular medical image fusion algorithms, the proposed algorithm was optimal for the four object indexes except for QAB/Findex, the mean of Piella, QAB/F and BSSIM indexes were 0.760 5, 0.877 1 and 0.537 3 respectively. The texture, edge and contrast of fused image were optimal. Subjective and objective analysis of the results showed the advantages of the proposed algorithm.
