Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering |
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Affiliation: | 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan, Kunming 650500, PR China;2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, 650500, PR China;3. College of Computer science and technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China;4. Department of Computer Science, University of Rochester, Rochester, NY 14623, United States;1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;3. School of Informatics and Computing, Indiana University-Purdue University Indianapolis, IN 46202, USA |
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Abstract: | Sparse representation (SR) has been widely used in image fusion in recent years. However, source image, segmented into vectors, reduces correlation and structural information of texture with conventional SR methods, and extracting texture with the sliding window technology is more likely to cause spatial inconsistency in flat regions of multi-modality medical fusion image. To solve these problems, a novel fusion method that combines separable dictionary optimization with Gabor filter in non-subsampled contourlet transform (NSCT) domain is proposed. Firstly, source images are decomposed into high frequency (HF) and low frequency (LF) components by NSCT. Then the HF components are reconstructed sparsely by separable dictionaries with iterative updating sparse coding and dictionary training. In the process, sparse coefficients and separable dictionaries are updated by orthogonal matching pursuit (OMP) and manifold-based conjugate gradient method, respectively. Meanwhile, the Gabor energy as weighting factor is utilized to guide the LF components fusion, and this further improves the fusion degree of low-significant feature in the flat regions. Finally, the fusion components are transformed to obtain fusion image by inverse NSCT. Experimental results demonstrate the more competitive results of the proposal, leading to the state-of-art performance on both visual quality and objective assessment. |
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Keywords: | Image fusion Multi-modality medical image Sparse representation Gabor filter Non-subsampled contourlet transform |
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