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Fully automatic Segmentation of Knee Cartilage on 3D MR images based on Knowledge of Shape and Intensity per Patch  

Park, Sang-Hyun (Automation and Systems Research Institute, School of Electrical Engineering and Computer Science, Seoul National University)
Lee, Soo-Chan (Automation and Systems Research Institute, School of Electrical Engineering and Computer Science, Seoul National University)
Shim, Hack-Joon (School of Electrical Engineering and Computer Science, Seoul National University)
Yun, Il-Dong (School of Digital Information Engineering, Hankuk University of Foreign Studies)
Lee, Sang-Uk (School of Electrical Engineering and Computer Science, Seoul National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.47, no.6, 2010 , pp. 75-81 More about this Journal
Abstract
The segmentation of cartilage is crucial for the diagnose and treatment of osteoarthritis (OA), and has mostly been done manually by an expert, requiring a considerable amount of time and effort due to the thin shape and vague boundaries of the cartilage in MR (magnetic resonance) images. In this paper, we propose a fully automatic method to segment cartilage in a knee joint on MR images. The proposed method is based on a small number of manually segmented images as the training set and comprised of an initial per patch segmentation process and a global refinement process on the cumulative per patch results. Each patch for per patch segmentation is positioned by classifying the bone-cartilage interface on the pre-segmented bone surface. Next, the shape and intensity priors are constructed for each patch based on information extracted from reference patches in the training set. The ratio of influence between the shape and intensity priors is adaptively determined per patch. Each patch is segmented by graph cuts, where energy is defined based on constructed priors. Finally, global refinement is conducted on the global cartilage using the results of per patch segmentation as the shape prior. Experimental evaluation shows that the proposed framework provide accurate and clinically useful segmentation results.
Keywords
3차원 영상 영역화;지역적 접근방식;그래프 컷;형상 및 밝기 사전지식;의료영상;
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