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Automatic Hepatic Tumor Segmentation Using Composite Hypotheses.
- Source :
- Image Analysis & Recognition; 2005, p922-929, 8p
- Publication Year :
- 2005
-
Abstract
- This paper proposes an automatic hepatic tumor segmentation method of a computed tomography (CT) image using composite hypotheses. The liver structure is first segmented using histogram transformation, multi-modal threshold, maximum a posteriori decision, and binary morphological filtering. Hepatic vessels are removed from the liver because hepatic vessels are not related to tumor segmentation. In order to find an optimal threshold, composite hypotheses and minimum total probability error are used. Then a hepatic tumor is segmented by using the optimal threshold value. In order to test the proposed method, 272 slices from 10 patients were selected. Experimental results show that the proposed method is very useful for diagnosis of the normal and abnormal liver. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540290698
- Database :
- Supplemental Index
- Journal :
- Image Analysis & Recognition
- Publication Type :
- Book
- Accession number :
- 32691192
- Full Text :
- https://doi.org/10.1007/11559573_112