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.
In order to remove neighboring abdominal organs of the liver, we propose an improved fully automatic liver segmentation using histogram tail threshold (HTT) algorithms. A region of interest of the liver is first segmented. A left HTT (LHTT) algorithm is performed to eliminate the pancreas, spleen, and left kidney. After the right kidney is eliminated by the right HTT (RHTT) algorithm, the robust liver structure is segmented. From the results of experiments, the improved automatic liver segmentation using HTT algorithms has strong similarity performance as manual segmentation by medical doctor.
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