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Conference Paper: Simultaneous 3-D segmentation of three bone compartments on high resolution knee MR images from osteoarthritis initiative (OAI) using graph cuts

TitleSimultaneous 3-D segmentation of three bone compartments on high resolution knee MR images from osteoarthritis initiative (OAI) using graph cuts
Authors
KeywordsGraph cuts
Knee bone
MR images
Osteoarthritis
Segmentation
Issue Date2009
Citation
Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2009, v. 7259, article no. 72593P How to Cite?
AbstractOsteoarthritis (OA) is associated with degradation of cartilage and related changes in the underlying bone. Quantitative measurement of those changes from MR images is an important biomarker to study the progression of OA and it requires a reliable segmentation of knee bone and cartilage. As the most popular method, manual segmentation of knee joint structures by boundary delineation is highly laborious and subject to user-variation. To overcome these difficulties, we have developed a semi-automated method for segmentation of knee bones, which consisted of two steps: placement of seeds and computation of segmentation. In the first step, seeds were placed by the user on a number of slices and then were propagated automatically to neighboring images. The seed placement could be performed on any of sagittal, coronal, and axial planes. The second step, computation of segmentation, was based on a graph-cuts algorithm where the optimal segmentation is the one that minimizes a cost function, which integrated the seeds specified by the user and both the regional and boundary properties of the regions to be segmented. The algorithm also allows simultaneous segmentation of three compartments of the knee bone (femur, tibia, patella). Our method was tested on the knee MR images of six subjects from the osteoarthritis initiative (OAI). The segmentation processing time (mean±SD) was (22±4)min, which is much shorter than that by the manual boundary delineation method (typically several hours). With this improved efficiency, our segmentation method will facilitate the quantitative morphologic analysis of changes in knee bones associated with osteoarthritis. © 2009 Copyright SPIE - The International Society for Optical Engineering.
Persistent Identifierhttp://hdl.handle.net/10722/316026
ISSN
2023 SCImago Journal Rankings: 0.226

 

DC FieldValueLanguage
dc.contributor.authorShim, Hackjoon-
dc.contributor.authorKwoh, C. Kent-
dc.contributor.authorYun, Il Dong-
dc.contributor.authorLee, Sang Uk-
dc.contributor.authorBae, Kyongtae-
dc.date.accessioned2022-08-24T15:48:59Z-
dc.date.available2022-08-24T15:48:59Z-
dc.date.issued2009-
dc.identifier.citationProgress in Biomedical Optics and Imaging - Proceedings of SPIE, 2009, v. 7259, article no. 72593P-
dc.identifier.issn1605-7422-
dc.identifier.urihttp://hdl.handle.net/10722/316026-
dc.description.abstractOsteoarthritis (OA) is associated with degradation of cartilage and related changes in the underlying bone. Quantitative measurement of those changes from MR images is an important biomarker to study the progression of OA and it requires a reliable segmentation of knee bone and cartilage. As the most popular method, manual segmentation of knee joint structures by boundary delineation is highly laborious and subject to user-variation. To overcome these difficulties, we have developed a semi-automated method for segmentation of knee bones, which consisted of two steps: placement of seeds and computation of segmentation. In the first step, seeds were placed by the user on a number of slices and then were propagated automatically to neighboring images. The seed placement could be performed on any of sagittal, coronal, and axial planes. The second step, computation of segmentation, was based on a graph-cuts algorithm where the optimal segmentation is the one that minimizes a cost function, which integrated the seeds specified by the user and both the regional and boundary properties of the regions to be segmented. The algorithm also allows simultaneous segmentation of three compartments of the knee bone (femur, tibia, patella). Our method was tested on the knee MR images of six subjects from the osteoarthritis initiative (OAI). The segmentation processing time (mean±SD) was (22±4)min, which is much shorter than that by the manual boundary delineation method (typically several hours). With this improved efficiency, our segmentation method will facilitate the quantitative morphologic analysis of changes in knee bones associated with osteoarthritis. © 2009 Copyright SPIE - The International Society for Optical Engineering.-
dc.languageeng-
dc.relation.ispartofProgress in Biomedical Optics and Imaging - Proceedings of SPIE-
dc.subjectGraph cuts-
dc.subjectKnee bone-
dc.subjectMR images-
dc.subjectOsteoarthritis-
dc.subjectSegmentation-
dc.titleSimultaneous 3-D segmentation of three bone compartments on high resolution knee MR images from osteoarthritis initiative (OAI) using graph cuts-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.812487-
dc.identifier.scopuseid_2-s2.0-71649101789-
dc.identifier.volume7259-
dc.identifier.spagearticle no. 72593P-
dc.identifier.epagearticle no. 72593P-

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