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首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >Fully automatic segmentation of left ventricle in a sequence of echocardiography images of one cardiac cycle by dynamic directional vector field convolution (DDVFC) method and manifold learning
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Fully automatic segmentation of left ventricle in a sequence of echocardiography images of one cardiac cycle by dynamic directional vector field convolution (DDVFC) method and manifold learning

机译：动态方向矢量场卷积（DDVFC）方法和流形学习在一个心动周期的一系列超声心动图图像中对左心室进行全自动分割

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摘要

In this paper, an automatic method for segmentation of the left ventricle in two-dimensional (2D) echocardiography images of one cardiac cycle is proposed. In the first step of this method, using a mean image of a sequence of echocardiography images and its statistical properties the approximate region of left ventricle (LV) is extracted. Then the coordinate of extracted rectangular (ROI) is applied on all frames of sequences automatically. The mean image extracted ROI is used for defining the initial contour by scanning from the center point in polar coordinate. In the next step, all the extracted ROIs from the frames are mapped in a 2D space using the nonlinear dimension reduction manifold learning method. Using the properties of the manifold map end diastole (ED) and end systole (ES) frames are determined. Segmentation of the frames begins from ES frame, utilizing the dynamic directional vector field convolution (DDVFC) level set method with the initial contour as mentioned above. Final contour of each segmented frame is used as the initial contour of the next frame. Maximum range of the active contour motion is limited by a percent of the Euclidean distance between the point corresponds the current frame and the previous one in the resultant manifold. The results obtained from our method are quantitatively evaluated to those obtained by the gold contours drawn by a cardiologist on 489 echocardiographic images of seven volunteers using four distance measures: Hausdorff distance, average distance, area difference and area coverage error. We have also compared our results with the results of applying only DDVFC method. Comparing the implementation of only the DDVFC method, the results show final contours by proposed method are more close to contours drawn by a cardiologist.
机译：本文提出了一种自动分割一个心动周期的二维（2D）超声心动图图像中的左心室的方法。在此方法的第一步中，使用一系列超声心动图图像的平均图像及其统计属性，提取左心室（LV）的近似区域。然后，将提取的矩形（ROI）坐标自动应用于序列的所有帧。通过从极坐标中的中心点扫描，将平均图像提取的ROI用于定义初始轮廓。下一步，使用非线性降维流形学习方法将所有从帧中提取的ROI映射到2D空间中。使用流形图的属性确定舒张末期（ED）和收缩末期（ES）的帧。帧的分割从ES帧开始，利用动态方向矢量场卷积（DDVFC）级别设置方法以及上述初始轮廓。每个分割帧的最终轮廓用作下一帧的初始轮廓。有效轮廓运动的最大范围受该点之间的欧几里得距离的百分比限制，该点对应于当前框架和结果歧管中的前一个框架。从我们的方法获得的结果将通过心脏病学家在四个志愿者测量的489个超声心动图图像上的四个黄金距离（Hausdorff距离，平均距离，面积差和面积覆盖误差）进行定量评估，以得出由心脏病专家绘制的黄金轮廓。我们还将我们的结果与仅应用DDVFC方法的结果进行了比较。比较仅DDVFC方法的实现，结果表明，所提出方法的最终轮廓更接近心脏病专家绘制的轮廓。

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