Sparse and Low-Rank Matrix Decomposition for Local Morphological Analysis to Diagnose Cirrhosis
Sparse and Low-Rank Matrix Decomposition for Local Morphological Analysis to Diagnose Cirrhosis
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DOI：
10.1587/transinf.2014edp7180
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Junping Deng;X. Han;Yenwei Chen;Gang Xu;Yoshinobu Sato;M. Hori;N. Tomiyama
中科院分区：
文献类型：
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作者：
Junping Deng;X. Han;Yenwei Chen;Gang Xu;Yoshinobu Sato;M. Hori;N. Tomiyama
慢性肝病是一个主要的全球性健康问题。慢性肝病的诊断和分期是一个重要的问题。在本文中，我们提出了一种定量分析局部形态学变化的方法，以准确和实用的计算机辅助诊断肝硬化。我们的方法是基于稀疏和低秩矩阵分解，因为肝脏形状的矩阵可以被分解成两部分：一个低秩矩阵，可以被认为是类似于正常肝脏，和一个稀疏的误差项，表示局部变形。与基于统计形状模型（SSM）的全局形态学分析策略相比，该方法提高了正常和异常分类的准确性。我们还建议使用稀疏误差项的范数作为正常或异常分类的简单度量。实验结果表明，该方法优于现有的SSM方法。关键词：局部形态分析，肝硬化诊断，稀疏低秩矩阵分解，增广拉格朗日乘子
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. key words: local morphological analysis, diagnosis of cirrhosis, sparse and low-rank matrix decomposition, augmented lagrange multiplier