Segmentation of Liver Metastases Using a Level Set Method with Spiral-Scanning Technique and Supervised Fuzzy Pixel Classification
Katholieke Universiteit Leuven
| Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1407 |
Submitted by Dirk Smeets on 07-06-2008.
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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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, |
| Toolkit: | ITK |
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| Segmentation of Liver Metastases in CT Scans by Adaptive Thresholding and Morphological Processing | ||
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