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Segmentation of Biomedical Images Using Active Contour Model with Robust Image Feature and Shape Prior

2014

Article

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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.

Author(s): S. Y. Yeo and X. Xie and I. Sazonov and P. Nithiarasu
Journal: International Journal for Numerical Methods in Biomedical Engineering
Volume: 30
Number (issue): 2
Pages: 232- 248
Year: 2014

Department(s): Perceiving Systems
Bibtex Type: Article (article)
Paper Type: Journal

BibTex

@article{jnb1,
  title = {Segmentation of Biomedical Images Using Active Contour Model with Robust Image Feature and Shape Prior},
  author = {Yeo, S. Y. and Xie, X. and Sazonov, I. and Nithiarasu, P.},
  journal = {International Journal for Numerical Methods in Biomedical Engineering},
  volume = {30},
  number = {2},
  pages = {232- 248},
  year = {2014}
}