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Automatic segmentation of meniscus using locally weighted voting based on multi-atlas and edge classification in knee MR images
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Paper 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.

Paper Details

Date Published: 27 March 2019
PDF: 4 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500M (27 March 2019); doi: 10.1117/12.2523712
Show Author Affiliations
SoonBeen Kim, Seoul Women's Univ. (Korea, Republic of)
Hyeonjin Kim, Seoul Women's Univ. (Korea, Republic of)
Helen Hong, Seoul Women's Univ. (Korea, Republic of)
Joon Ho Wang, Sungkyunkwan Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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