Segmentation of Liver Metastases in CT Scans by Adaptive Thresholding and Morphological Processing
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1419
This article presents an algorithm for the segmentation of liver metastases in CT scans. It is a hybrid method that combines adaptive thresholding based on a gray value analysis of the ROI with model-based morphological processing. We show the results of the MICCAI liver tumor segmentation competition 2008 which were successful for all ten tumors.

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plus my review by Xiang Deng on 07-25-2008 for revision #1
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Categories: Distance maps, Filtering, Mathematical Morphology, Region growing, Segmentation, Thresholding
Keywords: segmentation, liver metastases, MICCAI competition
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