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

  • Luyao Wang
  • Zhi Zhang
  • Jingjing Liu
  • Bo Jiang
  • Xiyao Duan
  • Qingguo Xie
  • Daoyu Hu
  • Zhen Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

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.

Keywords

CT liver images Texture feature Multiclass classification Support vector machine (SVM) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luyao Wang
    • 1
    • 2
  • Zhi Zhang
    • 1
  • Jingjing Liu
    • 1
    • 3
  • Bo Jiang
    • 1
  • Xiyao Duan
    • 1
  • Qingguo Xie
    • 1
    • 2
  • Daoyu Hu
    • 4
  • Zhen Li
    • 4
  1. 1.Department of Biomedical EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhanChina
  3. 3.College of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  4. 4.Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina

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