• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1096-1100
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• 2010

In this paper, we propose an automatic segmentation of the meniscus based on active shape model using interpolated shape information in MR images. First, the statistical shape model of meniscus is constructed to reflect the shape variation in the training set. Second, the generation technique of interpolated shape information by using the weight according to shape similarity is proposed to robustly segment the meniscus with large variation. Finally, the automatic meniscus segmentation is performed through the active shape model fitting. For the evaluation of our method, we performed the visual inspection, accuracy measure and processing time. For accuracy evaluation, the average distance difference between automatic segmentation and semi-automatic segmentation are calculated and visualized by color-coded mapping. Experimental results show that the average distance difference was $0.54{\pm}0.16mm$ in medial meniscus and $0.73{\pm}0.39mm$ in lateral meniscus. The total processing time was 4.87 seconds on average.

• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.29-38
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• 2018

In this paper, we propose an automatic segmentation method of meniscus in knee MR images by automatic meniscus localization, multi-atlas-based locally-weighted voting, and patch-based edge feature classification. First, after segmenting the bone and knee articular cartilage, the volume of interest of the meniscus is automatically localized. Second, the meniscus is segmented by multi-atlas-based locally-weighted voting taking into account the weights of shape and intensity distribution in the volume of interest of the meniscus. Finally, to remove leakage to the collateral ligaments with similar intensity, meniscus is refined using patch-based edge feature classification considering shape and distance weights. Dice similarity coefficient between proposed method and manual segmentation were 80.13% of medial meniscus and 80.81 % for lateral meniscus, and showed better results of 7.25% for medial meniscus and 1.31% for lateral meniscus compared to the multi-atlas-based locally-weighted voting.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.244-245
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• 2010