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Proceedings Paper

3-D segmentation of articular cartilages by graph cuts using knee MR images from osteoarthritis initiative
Author(s): Hackjoon Shim; Soochan Lee; Bohyeong Kim; Cheng Tao; Samuel Chang; Il Dong Yun; Sang Uk Lee; Kent Kwoh; Kyongtae Bae
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Paper Abstract

Knee osteoarthritis is the most common debilitating health condition affecting elderly population. MR imaging of the knee is highly sensitive for diagnosis and evaluation of the extent of knee osteoarthritis. Quantitative analysis of the progression of osteoarthritis is commonly based on segmentation and measurement of articular cartilage from knee MR images. Segmentation of the knee articular cartilage, however, is extremely laborious and technically demanding, because the cartilage is of complex geometry and thin and small in size. To improve precision and efficiency of the segmentation of the cartilage, we have applied a semi-automated segmentation method that is based on an s/t graph cut algorithm. The cost function was defined integrating regional and boundary cues. While regional cues can encode any intensity distributions of two regions, "object" (cartilage) and "background" (the rest), boundary cues are based on the intensity differences between neighboring pixels. For three-dimensional (3-D) segmentation, hard constraints are also specified in 3-D way facilitating user interaction. When our proposed semi-automated method was tested on clinical patients' MR images (160 slices, 0.7 mm slice thickness), a considerable amount of segmentation time was saved with improved efficiency, compared to a manual segmentation approach.

Paper Details

Date Published: 11 March 2008
PDF: 9 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691448 (11 March 2008); doi: 10.1117/12.770887
Show Author Affiliations
Hackjoon Shim, Univ. of Pittsburgh (United States)
Soochan Lee, Seoul National Univ. (South Korea)
Bohyeong Kim, Seoul National Univ. (South Korea)
Cheng Tao, Univ. of Pittsburgh (United States)
Samuel Chang, Univ. of Pittsburgh (United States)
Il Dong Yun, Hankuk Univ. of Foreign Studies (South Korea)
Sang Uk Lee, Seoul National Univ. (South Korea)
Kent Kwoh, Univ. of Pittsburgh (United States)
Kyongtae Bae, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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