We propose an automatic segmentation of meniscus from knee MR images using multi-atlas segmentation and patchbased edge classification. To prevent registration to large tissues, meniscus is targeted using segmented bone and articular cartilage information. To segment the meniscus with large shape variations and remove leakage to the collateral ligaments robustly, meniscus is segmented using shape- and intensity-based locally-weighted voting (LWV) and patchbased edge classification. Experimental result shows that the Dice similarity coefficient of proposed method as comparison with two manually outlining results provides over 80% in average and is improved compared to LWV based on multi-atlas.
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