Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set.
Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set.
复制标题
使用稀疏矩阵变换和水平集自动分割右心室超声图像。
DOI：
10.1088/0031-9155/58/21/7609
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发表时间：
2013-11-07
影响因子：
3.5
通讯作者：
Fei B
中科院分区：
文献类型：
--
作者：
Qin X;Cong Z;Fei B
提出了一种自动分割框架来分割超声心动图图像中的右心室（RV）。该方法可以通过结合稀疏矩阵变换、训练模型和基于局部区域的水平集，从连续超声心动图系列中自动分割心外膜和心内膜边界。首先，稀疏矩阵变换通过分析图像的统计信息，提取心肌的主要运动区域作为特征图像。其次，将 RV 训练模型注册到特征图像，以定位 RV 的位置。第三，调整训练模型，然后作为每个图像分割的优化初始化。最后，基于初始化，应用局部的、基于区域的水平集算法来分割每个超声心动图中的心外膜和心内膜边界。使用三种评估方法来验证分割框架的性能。 Dice 系数衡量手动和自动分割之间的总体一致性。使用手动和自动分割的边界之间的绝对距离和豪斯多夫距离来衡量分割的准确性。使用人类受试者的超声图像进行验证。对于心外膜和心内膜边界，Dice系数分别为90.8±1.7%和87.3±1.9%，绝对距离分别为2.0±0.42mm和1.79±0.45mm，Hausdorff距离分别为6.86±1.71mm和7.02±1.17mm。基于稀疏矩阵变换和水平集的自动分割方法可以为定量心脏成像提供有用的工具。
An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 ± 1.7% and 87.3 ± 1.9%, the absolute distances were 2.0 ± 0.42 mm and 1.79 ± 0.45 mm, and the Hausdorff distances were 6.86 ± 1.71 mm and 7.02 ± 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.
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