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

Smeets, Dirk, Stijnen, Bert, Loeckx, Dirk, De Dobbelaer, Bart, Suetens, Paul
Katholieke Universiteit Leuven, ESAT - PSI
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1407
New: Prefer using the following doi: https://doi.org/10.54294/dxbugc
Published in The MIDAS Journal - MICCAI 2008 Workshop: Grand Challenge Liver Tumor Segmentation.
Submitted by Dirk Smeets on 2008-07-06T16:33:21Z.

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.