Introduction to signal processing
Introduction to signal processing
Applied Numerical Methods for Engineers Using MATLAB
Applied Numerical Methods for Engineers Using MATLAB
Computer Vision
Digital Image Processing
Random Processes: Filtering, Estimation, and Detection
Random Processes: Filtering, Estimation, and Detection
Efficient liver segmentation based on the spine
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Automatic Segmentation of Single and Multiple Neoplastic Hepatic Lesions in CT Images
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Automatic Segmentation of Neoplastic Hepatic Disease Symptoms in CT Images
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
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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.