The use of echocardiography ventricle segmentation can obtain ventricular volume parameters, and it is helpful to evaluate cardiac function. However, the ultrasound images have the characteristics of high noise and difficulty in segmentation, bringing huge workload to segment the object region manually. Meanwhile, the automatic segmentation technology cannot guarantee the segmentation accuracy. In order to solve this problem, a novel algorithm framework is proposed to segment the ventricle. Firstly, faster region-based convolutional neural network is used to locate the object to get the region of interest. Secondly, K-means is used to pre-segment the image; then a mean shift with adaptive bandwidth of kernel function is proposed to segment the region of interest. Finally, the region growing algorithm is used to get the object region. By this framework, ventricle is obtained automatically without manual localization. Experiments prove that this framework can segment the object accurately, and the algorithm of adaptive mean shift is more stable and accurate than the mean shift with fixed bandwidth on quantitative evaluation. These results show that the method in this paper is helpful for automatic segmentation of left ventricle in echocardiography.
利用超声心动图进行心室分割能够获得心室容积参数，对评价心功能有重要意义。但超声图像有噪声大、难以分割等特点，仅仅靠人工对目标区域进行手动分割工作量巨大，且目前自动分割技术尚无法保证分割精度。针对这些问题，本文提出了一种全新的算法框架对心室结构进行了分割提取。首先，采用更快速的基于区域的卷积神经网络目标检测算法对目标区域进行定位，得到感兴趣区域；然后使用 K 均值（ K-means）算法对目标区域进行初始聚类；接着使用一种自适应核函数带宽的均值漂移（mean shift）算法进行分割；最后采用种子填充算法提取目标区域。该算法结构实现了自动提取分割目标区域，免去了人工定位的过程。实验表明，在定量评价标准下，这种分割框架能够对目标区域进行精确的提取，同时提出的自适应均值漂移算法较传统固定带宽均值漂移算法更稳定，且分割效果更好。研究结果显示，本文所述方法有助于实现超声心动图左心室切面的自动分割。.
Keywords: adaptive bandwidth; left ventricle localization; left ventricle segmentation; mean shift segmentation; pixel clustering.