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Abstract #1533

Automated Segmentation of Knee Cartilage from MR Images using Sequential Multi-atlas Registration and Shape-constrained Locally-weighted Voting for Quantitative Knee Joint Assessment

Han Sang Lee1, Helen Hong2, Young Cheol Yoon3, and Junmo Kim1

1School of Electrical Engineering, KAIST, Daejeon, Korea, Republic of, 2Dept. of Software Convergence, Seoul Women's University, Seoul, Korea, Republic of, 3Department of Radiology, Samsung Medical Center, Seoul, Korea, Republic of

We propose a multi-atlas segmentation method for the knee cartilage in T2 PD MR images using sequential multi-atlas registrations and locally-weighted voting (LWV). To select training atlases similar to the test image, a 2D projection image-based atlas selection method is proposed. Then, to extract a bone model to be used as registration target in cartilage segmentation, the bone is segmented by sequential multi-atlas registrations and LWV. Finally, to segment a cartilage without leakage into low-contrast surroundings, the cartilage is segmented by bone-mask-based cartilage registration and shape-constrained LWV with distance and structure similarity weights, as well as atlas similarity weight.

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