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| Medical Image Features Extraction and Fusion Based on Online Dictionary Learning |
| China Faculty of Electronic and Electrical Engineering, Dalian University of Technology, Dalian 116024, China |
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Abstract An image features extraction and fusion algorithm based on online dictionary learning (ODL) is presented in this paper. Firstly, source images were combined into a joint matrix by the sliding window technique, the size of the sliding window was 8×8, the overcomplete dictionary was trained by ODL algorithm and the sparse codes were acquired by LARS algorithm; the activity level measurement of sparse codes was the L1 norm of its vector, then, the sparse codes were fused by activity level maximum rule; finally, the fused image was reconstructed by overcomplete dictionary and fused sparse codes. Coaligned medical images of twenty patients were tested by experiments and the quality of the fused image was evaluated by five kinds of commonly used objective criterions. Compared with the other two popular medical image fusion algorithms, objective criterions of the fusion result show the advantage of the proposed algorithm, the mean of Piella, QAB/F，MIAB/F， BSSIM and space frequency index is 0.800 4, 0.552 4,
3.630 2, 0.726 9 and 31.941 3, the fusion images of the proposed algorithm have high definition and contrast, clear texture and edge and fast speed, showing its application potentials of aiding clinical diagnoses and treatment.
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