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| Brain Image Fusion Based on Online Dictionary Learning and Pulse Coupled Neural Network |
1 Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China
2 School of Electrical & Information Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China |
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Abstract Medical image fusion is an important issue in the field of medical imaging and radiation medicine, and is widely recognized by medical and engineering fields. In this study, a new algorithm of brain CT and MR image fusion was proposed based on the online dictionary learning (ODL) and pulse coupled neural network (PCNN). First, the source images were divided into patches using the sliding window technology, and then the ODL algorithm and LARS algorithm were used to get the sparse coding coefficient of corresponding column vector of each image patch. Second, the sparse coding coefficient was selected as the external stimulus input of the pulse coupled neural network for iterative processing, and the fusion coefficient was determined by firing times. At last, the fusion image was reconstructed according to the fusion coefficients and the learned dictionary. Based on CT and MR brain data of 10 groups that from the Harvard Medical School, and compared with the KSVD-based fusion method, the ODL-based fusion method and the NSCT-based fusion method, the experimental results showed that the proposed algorithm was superior to the other algorithms in terms of subjective visual effects and objective evaluation index. The average of objective parameters BSSIM, MI, Piella, SF, STD, QAB/F were 0.751 2, 3.769 6, 0.697 1, 29.526 7, 90.090 6, 0.570 7, and the fusion results can provide abundant information to help medical doctors to analyze the pathological tissue, thus it can improve the accuracy of clinical medical diagnosis and make efficient treatment plans.
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