Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 23, Issue 2, Pages 222-226Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2015.2508039
Keywords
Edge-based active contour; edge-stop function; gradient information; image segmentation; probability score
Categories
Funding
- A*STAR Singapore-China Joint Research Programme [12105009]
- WBS [R265-000-467-305]
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Edge-based active contour models are effective in segmenting images with intensity inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as in medical images. Traditional edge-stop functions (ESFs) utilize only gradient information, which fails to stop contour evolution at such boundaries because of the small gradient magnitudes. To address this problem, we propose a framework to construct a group of ESFs for edge-based active contour models to segment objects with poorly defined boundaries. In our framework, which incorporates gradient information as well as probability scores from a standard classifier, the ESF can be constructed from any classification algorithm and applied to any edge-based model using a level set method. Experiments on medical images using the distance regularized level set for edge-based active contour models as well as the k-nearest neighbours and the support vector machine confirm the effectiveness of the proposed approach.
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