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首页> 外文期刊>Medical image analysis >Combinative multi-scale level set framework for echocardiographic image segmentation.
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Combinative multi-scale level set framework for echocardiographic image segmentation.

机译：超声心动图图像分割的多尺度水平组合框架。

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摘要

In the automatic segmentation of echocardiographic images, a priori shape knowledge has been used to compensate for poor features in ultrasound images. This shape knowledge is often learned via an off-line training process, which requires tedious human effort and is highly expertise-dependent. More importantly, a learned shape template can only be used to segment a specific class of images with similar boundary shape. In this paper, we present a multi-scale level set framework for segmentation of endocardial boundaries at each frame in a multiframe echocardiographic image sequence. We point out that the intensity distribution of an ultrasound image at a very coarse scale can be approximately modeled by Gaussian. Then we combine region homogeneity and edge features in a level set approach to extract boundaries automatically at this coarse scale. At finer scale levels, these coarse boundaries are used to both initialize boundary detection and serve as an external constraint to guide contour evolution. This constraint functions similar to a traditional shape prior. Experimental results validate this combinative framework.
机译：在超声心动图图像的自动分割中，先验形状知识已用于补偿超声图像中的不良特征。这种形状知识通常是通过离线培训过程来学习的，这需要人工的繁琐工作并且高度依赖于专业知识。更重要的是，学习的形状模板只能用于分割具有相似边界形状的特定类别的图像。在本文中，我们提出了一种用于在多帧超声心动图图像序列中每帧心内膜边界进行分割的多尺度水平集框架。我们指出，可以通过高斯近似模拟非常粗略的超声图像的强度分布。然后，我们在水平集方法中结合区域均匀性和边缘特征，以自动在此粗尺度上提取边界。在较细的比例级别上，这些粗略边界既可用于初始化边界检测，又可作为指导轮廓演变的外部约束。该约束的功能类似于传统的形状先验。实验结果验证了该组合框架。

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