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Conference Paper: 3-D segmentation of articular cartilages by graph cuts using knee MR images from the osteoarthritis initiative

Title3-D segmentation of articular cartilages by graph cuts using knee MR images from the osteoarthritis initiative
Authors
KeywordsGraph cuts
Knee cartilage
MR images
Segmentation
Issue Date2008
Citation
Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2008, v. 6914, article no. 691448 How to Cite?
AbstractKnee 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.
Persistent Identifierhttp://hdl.handle.net/10722/315999
ISSN
2023 SCImago Journal Rankings: 0.226

 

DC FieldValueLanguage
dc.contributor.authorShim, Hackjoon-
dc.contributor.authorLee, Soochan-
dc.contributor.authorKim, Bohyeong-
dc.contributor.authorTao, Cheng-
dc.contributor.authorChang, Samuel-
dc.contributor.authorYun, Il Dong-
dc.contributor.authorLee, Sang Uk-
dc.contributor.authorKwoh, Kent-
dc.contributor.authorBae, Kyongtae-
dc.date.accessioned2022-08-24T15:48:54Z-
dc.date.available2022-08-24T15:48:54Z-
dc.date.issued2008-
dc.identifier.citationProgress in Biomedical Optics and Imaging - Proceedings of SPIE, 2008, v. 6914, article no. 691448-
dc.identifier.issn1605-7422-
dc.identifier.urihttp://hdl.handle.net/10722/315999-
dc.description.abstractKnee 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.-
dc.languageeng-
dc.relation.ispartofProgress in Biomedical Optics and Imaging - Proceedings of SPIE-
dc.subjectGraph cuts-
dc.subjectKnee cartilage-
dc.subjectMR images-
dc.subjectSegmentation-
dc.title3-D segmentation of articular cartilages by graph cuts using knee MR images from the osteoarthritis initiative-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.770887-
dc.identifier.scopuseid_2-s2.0-43449131849-
dc.identifier.volume6914-
dc.identifier.spagearticle no. 691448-
dc.identifier.epagearticle no. 691448-

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