Mr. Sang Hyun Park Profile

Sang Hyun Park

at Seoul National Univ

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Proceedings Article | 13 March 2013 Paper

Interactive 3D segmentation method based on uncertain local region updating in hierarchical MRF graph

Sang Hyun Park, Il Dong Yun

Proceedings Volume 8669, 866942 (2013) https://doi.org/10.1117/12.2006718

KEYWORDS: Image segmentation, Magnetorheological finishing, Magnetic resonance imaging, Medical imaging, Bone, 3D modeling, 3D image processing, Pathology, Image processing, Current controlled current source

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In this paper, we present a three-dimensional interactive segmentation method. Unlike most previous interactive methods which largely depend on user interaction, we exploit a prior knowledge of training data to reduce the user effort. Based on the prior knowledge, most distinguishable parts of an object are automatically segmented and labels of some uncertain parts are queried to an user. To systematically model the problem, we combine the hierarchical Markov random field (HMRF) framework and the active learning scheme. The HMRF framework, proposed for the automatic manner, simultaneously reflects characteristics of local variations and their global smoothness, while the active learning scheme improves the efficiency of interactive system. We incorporate the active learning strategy into the editing step of the HMRF structure in order to find and modify the uncertain parts after the automatic segmentation. Specifically, the uncertainties of local regions are firstly computed by the label difference between segmentation candidates. Then, the graph models of the uncertain regions are updated by the user interaction. Since the HMRF structure constrains the smoothness of local regions and the global optimality, the segmentation is updated as a whole even though the small numbers of local parts are edited. The proposed method is applied to the segmentation of femur and tibia in knee MR images for evaluation. The evaluation demonstrates that the proposed method improves the segmentation efficiency more than the graph cut based method or manual editing.

Proceedings Article | 14 February 2012 Paper

Automatic bone segmentation in knee MR images using a coarse-to-fine strategy

Sang Hyun Park, Soochahn Lee, Il Dong Yun, Sang Uk Lee

Proceedings Volume 8314, 831405 (2012) https://doi.org/10.1117/12.910868

KEYWORDS: Image segmentation, Bone, Magnetic resonance imaging, Image resolution, Cartilage, Tissues, Image registration, 3D image processing, Magnetism, Image processing

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Segmentation of bone and cartilage from a three dimensional knee magnetic resonance (MR) image is a crucial element in monitoring and understanding of development and progress of osteoarthritis. Until now, various segmentation methods have been proposed to separate the bone from other tissues, but it still remains challenging problem due to different modality of MR images, low contrast between bone and tissues, and shape irregularity. In this paper, we present a new fully-automatic segmentation method of bone compartments using relevant bone atlases from a training set. To find the relevant bone atlases and obtain the segmentation, a coarse-to-fine strategy is proposed. In the coarse step, the best atlas among the training set and an initial segmentation are simultaneously detected using branch and bound tree search. Since the best atlas in the coarse step is not accurately aligned, all atlases from the training set are aligned to the initial segmentation, and the best aligned atlas is selected in the middle step. Finally, in the fine step, segmentation is conducted as adaptively integrating shape of the best aligned atlas and appearance prior based on characteristics of local regions. For experiment, femur and tibia bones of forty test MR images are segmented by the proposed method using sixty training MR images. Experimental results show that a performance of the segmentation and the registration becomes better as going near the fine step, and the proposed method obtain the comparable performance with the state-of-the-art methods.

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