Fully automatic segmentation based on localizing active contour method

Authors:
Toan Bui;Chunsoo Ahn;Yong-woo Lee;Jitae Shin
Affiliations:
Sungkyunkwan University, Suwon, South Korea;Sungkyunkwan University, Suwon, South Korea;Sungkyunkwan University, Suwon, South Korea;Sungkyunkwan University, Suwon, South Korea
Venue:
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
Year:
2014

Citing 8
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Geodesic Active Contours

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Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

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Minimization of Region-Scalable Fitting Energy for Image Segmentation

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Localizing Region-Based Active Contours

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Level set evolution with locally linear classification for image segmentation

Pattern Recognition

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Abstract

Cartilage segmentation is one of challenging issues because knee magnetic resonance (MR) images are consisted of thin sheet structure, intensity inhomogeneity, and low contrast between cartilage and muscle. In this paper, a fully automatic segmentation method for knee cartilage is proposed using spatial fuzzy c-mean clustering (SFCM) and morphological operators. The proposed method modifies the way to generate an approximate boundary of cartilage region, and combines it with localizing region-based active contour method, and overcomes limitations of previous methods. The performance of the proposed method is improved more than 10.8% by Dice similarity coefficient (DSC) in comparison with previous methods.