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| Simultaneous Medical Image Fusion and De-Noising with Joint Sparse Representation |
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
3 Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian 116027, Liaoning, China
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Abstract The complementary information of multi-modality medical images can be integrated together, which can provide abundant information and effective help for clinical diagnosis and treatment. Based on the joint sparse model, a new medical image fusion algorithm based on the joint sparse representation was proposed in this paper, and this method could carry out image fusion and de-noising simultaneously while the images were corrupted by noise. First, the registered source images were compiled into column vectors and composed of a joint matrix, and then an over-complete dictionary was obtained through online dictionary learning algorithm(ODL). Second, a joint dictionary was obtained by the over-complete dictionary under the joint sparse model, then based on the joint dictionary, the common sparse coefficients and unique sparse coefficients were computed by the least angle regression algorithm(LARS), and the sparse coefficients of fused image were obtained according to the fusion rule "choose max". Last, the fusion image was reconstructed according to the fusion coefficient and the over-complete dictionary. Compared with three classical algorithms,the results showed that the proposed algorithm has small luminance distortion, small contrast distortion and clear edge texture in the subjective vision, the statistical mean values of the objective parameters MI, QAB/F under noiseless and noisy case were 3.992 3, 2.896 4, 2.505 5 and 0.658, 0.552 4, 0.439 6, respectively. All of these can provide effective help for clinical diagnosis and treatment.
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