Automatic hepatic tumor segmentation using statistical optimal threshold

Authors:
Seung-Jin Park;Kyung-Sik Seo;Jong-An Park
Affiliations:
Dept. of Biomedical Engineering, Chonnam National University Medical School, Gwangju, Korea;Dept. of Electrical & Computer Engineering, New Mexico State University, Las Cruces, NM;Dept. of Information & Communications Engineering, Chosun University, Gwangju, Korea
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Year:
2005

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Abstract

This paper proposes an automatic hepatic tumor segmentation method of a computed tomography (CT) image using statistical optimal threshold. 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. Statistical optimal threshold is calculated by a transformed mixture probability density and minimum total probability error. Then a hepatic tumor is segmented using the optimal threshold value. In order to test the proposed method, 262 slices from 10 patients were selected. Experimental results show that the proposed method is very useful for diagnosis of the normal and abnormal liver.