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- W2214821036 abstract "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." @default.
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- W2214821036 date "2016-02-01" @default.
- W2214821036 modified "2023-10-17" @default.
- W2214821036 title "Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation" @default.
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- W2214821036 doi "https://doi.org/10.1109/lsp.2015.2508039" @default.
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