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27 March 2019 Automatic segmentation of meniscus using locally weighted voting based on multi-atlas and edge classification in knee MR images
SoonBeen Kim, Hyeonjin Kim, Helen Hong, Joon Ho Wang
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Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500M (2019) https://doi.org/10.1117/12.2523712
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore
Abstract
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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SoonBeen Kim, Hyeonjin Kim, Helen Hong, and Joon Ho Wang "Automatic segmentation of meniscus using locally weighted voting based on multi-atlas and edge classification in knee MR images", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500M (27 March 2019); https://doi.org/10.1117/12.2523712
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KEYWORDS
Image segmentation
Magnetic resonance imaging
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