Segmentation of Liver Metastases Using a Level Set Method with Spiral-Scanning Technique and Supervised Fuzzy Pixel Classification
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1407
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.

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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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