Segmentation of Liver Metastases Using a Level Set Method with Spiral-Scanning Technique and Supervised Fuzzy Pixel Classification

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In this paper a specific method is presented to facilitate the semi-automatic segmentation of liver metastases in CT images. Accurate and reliable segmentation of tumors is e.g. essential for the follow-up of cancer treatment. The core of the algorithm is a level set function. The initialization is provided by a spiral-scanning technique based on dynamic programming. The level set evolves according to a speed image that is the result of a statistical pixel classification algorithm with supervised learning. This method is tested on CT images of the abdomen and compared with manual delineations of liver tumors.

plus my review by Xiang Deng on 07-25-2008 for revision #9
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Categories: Distance maps, Feature extraction, Level sets, Probability, Segmentation, Statistical shape models, Surface extraction, Thresholding
Keywords: liver tumor, segmentation, level set, spiral-scanning technique, liver metastases
Toolkits: ITK
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