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• W2319319550 abstract "Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions. The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use Analysis K-SVD to train the dictionary and find the sparse coefficients. We utilize image patches to construct the analysis dictionary and then we employ the proposed sparse similarity measure to find a non-rigid transformation using free form deformation (FFD). Experimental results show that the proposed approach is able to robustly register 2D and 3D images in both simulated and real cases. The proposed method outperforms other state-of-the-art similarity measures and decreases the transformation error compared to the previous methods. Even in the presence of bias field distortion, the proposed method aligns images without any preprocessing." @default.
• W2319319550 created "2016-06-24" @default.
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• W2319319550 date "2016-06-01" @default.
• W2319319550 modified "2023-09-27" @default.
• W2319319550 title "Medical image registration using sparse coding of image patches" @default.
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• W2319319550 doi "https://doi.org/10.1016/j.compbiomed.2016.03.022" @default.
• W2319319550 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27085311" @default.
• W2319319550 hasPublicationYear "2016" @default.
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