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Disease discrimination based on disease subspace of organ shape using orthogonal complement of normal subspace

机译:基于正常子空间正交互补的器官形状疾病子空间疾病识别

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Diagnostic modeling based on computational anatomy is an important topic. In previous work, discrimination method using support vector machine based on principal component analysis of the hippocampus shapes have been proposed. However, disease-specific component was not considered explicitly. In this paper, we propose a method for constructing the disease subspace using orthogonal complement of the normal subspace. The proposed method was tested using the hepatic cirrhosis and hip osteoarthritis datasets and was compared to a previous method. In our experiments, the proposed method was effective for disease discrimination based on organ shapes.
机译:基于计算解剖学的诊断建模是一个重要的主题。在先前的工作中,已经提出了基于海马形状的主成分分析的使用支持向量机的判别方法。但是,没有明确考虑疾病特异性成分。在本文中,我们提出了一种使用正常子空间的正交补码构造疾病子空间的方法。使用肝硬化和髋骨关节炎数据集对提出的方法进行了测试,并与以前的方法进行了比较。在我们的实验中,提出的方法对于基于器官形状的疾病识别是有效的。

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