Segmentation of biomedical images using active contour model with robust image feature and shape prior.
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.
Institute of High Performance Computing, 1 Fusionopolis Way, Singapore 138632, Singapore; College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
Si Yong
SY
Yeo
Xianghua
X
Xie
Igor
I
Sazonov
Perumal
P
Nithiarasu
D000465:Algorithms; D001499:Bayes Theorem; D020811:Biomedical Technology / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical
Bayesian inference; active contour; image segmentation; level set model; shape distribution