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Discriminating normal and abnormal left ventricular shapes in four-chamber view 2D echocardiography

机译:在四腔视图二维超声心动图中区分正常和异常的左心室形状

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In this paper, we address discrimination between normal and abnormal left ventricular shapes by capturing deviations from the normal appearance through a new parametric distorted elliptic shape model. To apply the parametric description, we automatically locate the left ventricular region in 4-chamber views and extract its bounding contours and pose. The parametric description of the elliptic fit with minimum alignment error with the bounding contour then becomes the shape descriptor for the bounding contour. Labeled vectors from normal and damaged left ventricular regions are separated into two classes using a support vector machine. Results are presented on a large database of normal and abnormal left ventricular images showing the effectiveness of the parametric features for normal/abnormal discrimination.
机译:在本文中,我们通过新的参数化变形椭圆形模型捕获与正常外观的偏差,从而解决了正常和异常左心室形状之间的区别。为了应用参数描述,我们自动在4腔视图中定位左心室区域,并提取其边界轮廓和姿势。椭圆拟合的参数描述(具有与边界轮廓的最小对齐误差)随后成为边界轮廓的形状描述符。使用支持向量机将正常和受损左心室区域的标记向量分为两类。结果显示在大型的正常和异常左心室图像数据库中,显示出参数特征对正常/异常判别的有效性。

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