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Segmentation of biomedical images using active contour model with robust image feature and shape prior / Si Yong Yeo; Xianghua Xie; Igor Sazonov; Perumal Nithiarasu

International Journal for Numerical Methods in Biomedical Engineering, Volume: 30, Issue: 2, Pages: 232 - 248

Swansea University Authors: Xianghua, Xie, Igor, Sazonov, Perumal, Nithiarasu

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DOI (Published version): 10.1002/cnm.2600

Abstract

In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing...

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Published in: International Journal for Numerical Methods in Biomedical Engineering
ISSN: 2040-7939
Published: 2014
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URI: https://cronfa.swan.ac.uk/Record/cronfa20249
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Abstract: In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing with image noise, weak edges, and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the shape model to handle relatively large shape variations. The segmentation of various shapes from both synthetic and real images depict the robustness and efficiency of the proposed method.
College: College of Engineering
Issue: 2
Start Page: 232
End Page: 248