Classification of Hepatic Tissues from CT Images Based on Texture Features and Multiclass Support Vector Machines

Authors:
Luyao Wang;Zhi Zhang;Jingjing Liu;Bo Jiang;Xiyao Duan;Qingguo Xie;Daoyu Hu;Zhen Li
Affiliations:
Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China 430074 and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Techn ...;Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China 430074 and College of Computer Science and Technology, Huazhong University of Science and Technolo ...;Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China 430074 and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Techn ...;Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 430030;Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 430030
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

A computer-aided diagnosis (CAD) of X-ray Computed Tomography (CT) liver images with contrast agent injection is presented. Regions of interests (ROIs) on CT liver images are defined by experienced radiologists. For each ROI, texture features based on first order statistics (FOS), spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and gray level difference matrix (GLDM) are extracted. Support vector machine (SVM) is originally for binary classification. In order to classify hepatic tissues from CT images into primary hepatic carcinoma, hemangioma and normal liver, we utilize two methods to construct multiclass SVMs: one-against-all (OAA), one-against-one (OAO) and compare their performance. The result shows that a total accuracy rate of 97.78% is obtained with the multiclass SVM using the OAO method. Our study has some practical significance for clinical diagnosis.