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首页> 外文会议>2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining >Statistical shape model of the liver and effective mode selection for classification of liver cirrhosis
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Statistical shape model of the liver and effective mode selection for classification of liver cirrhosis

机译：肝的统计形状模型和肝硬化分类的有效模式选择

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In computational anatomy, statistical shape model is used for quantitative evaluation of the variations of an organ shape. Since liver cirrhosis will cause significant hepatic morphological changes, we applied statistical shape model of the liver to capture the morphological changes and recognize whether a liver is normal or abnormal. In this paper, we propose an effective mode selection method to improve the classification accuracy. In addition to the conventional Accumulated Variance Contribution Rate (AVCR) based mode selection, we newly propose a Pearson correlation based mode selection method and combine them to select the effective modes. The coefficients of the selected modes (components) are used as features to recognize whether liver is normal or abnormal. The effectiveness of the proposed method is evaluated by the classification accuracy of normal and abnormal. Experimental results show that our proposed method is superior than conventional methods.
机译：在计算解剖学中，统计形状模型用于定量评估器官形状的变化。由于肝硬化会引起明显的肝脏形态变化，因此我们应用了肝脏的统计形状模型来捕获形态变化并识别肝脏是正常还是异常。本文提出了一种有效的模式选择方法，以提高分类的准确性。除了基于传统的累积方差贡献率（AVCR）的模式选择外，我们还提出了一种基于Pearson相关的模式选择方法，并将其组合以选择有效模式。所选模式（成分）的系数用作识别肝脏是正常还是异常的特征。该方法的有效性通过正常和异常的分类精度来评估。实验结果表明，本文提出的方法优于常规方法。

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