Sparse and Low-Rank Matrix Decomposition for Local Morphological Analysis to Diagnose Cirrhosis

DOI Web Site 参考文献16件
  • DENG Junping
    College of Information Science and Engineering, Ritsumeikan University
  • HAN Xian-Hua
    College of Information Science and Engineering, Ritsumeikan University
  • CHEN Yen-Wei
    College of Information Science and Engineering, Ritsumeikan University Department of Radiology, Graduate School of Medicine, Osaka University
  • XU Gang
    College of Information Science and Engineering, Ritsumeikan University
  • SATO Yoshinobu
    Graduate School of Information Science, Nara Institute of Science and Technology (NAIST)
  • HORI Masatoshi
    Department of Radiology, Graduate School of Medicine, Osaka University
  • TOMIYAMA Noriyuki
    Department of Radiology, Graduate School of Medicine, Osaka University

抄録

Chronic liver disease is a major worldwide health problem. Diagnosis and staging of chronic liver diseases is an important issue. In this paper, we propose a quantitative method of analyzing local morphological changes for accurate and practical computer-aided diagnosis of cirrhosis. Our method is based on sparse and low-rank matrix decomposition, since the matrix of the liver shapes can be decomposed into two parts: a low-rank matrix, which can be considered similar to that of a normal liver, and a sparse error term that represents the local deformation. Compared with the previous global morphological analysis strategy based on the statistical shape model (SSM), our proposed method improves the accuracy of both normal and abnormal classifications. We also propose using the norm of the sparse error term as a simple measure for classification as normal or abnormal. The experimental results of the proposed method are better than those of the state-of-the-art SSM-based methods.

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