Yan P, Jia C X, Sinusas A, Thiele K, O'Donnell M, Duncan J S
Yale University, University of Michigan, Philips Research, University of Washington, USA.
Inf Process Med Imaging. 2007;20:233-44. doi: 10.1007/978-3-540-73273-0_20.
LV segmentation is often an important part of many automated cardiac diagnosis strategies. However, the segmentation of echocardiograms is a difficult task because of poor image quality. In echocardiography, we note that radio-frequency (RF) signal is a rich source of information about the moving LV as well. In this paper, first, we will investigate currently used, important RF derived parameters: integrated backscatter coefficient (IBS), mean central frequency (MCF) and the maximum correlation coefficients (MCC) from speckle tracking. Second, we will develop a new segmentation algorithm for the segmentation of the LV boundary, which can avoid local minima and leaking through uncompleted boundary. Segmentations are carried out on the RF signal acquired from a Sonos7500 ultrasound system. The results are validated by comparing to manual segmentation results.
左心室分割通常是许多自动心脏诊断策略的重要组成部分。然而，由于图像质量较差，超声心动图的分割是一项艰巨的任务。在超声心动图中，我们注意到射频（RF）信号也是有关运动左心室的丰富信息源。在本文中，首先，我们将研究当前使用的重要的从射频导出的参数：综合背向散射系数（IBS）、平均中心频率（MCF）以及散斑跟踪的最大相关系数（MCC）。其次，我们将开发一种用于左心室边界分割的新算法，该算法可以避免局部最小值并防止通过未完成的边界泄漏。分割是在从Sonos7500超声系统采集的射频信号上进行的。通过与手动分割结果进行比较来验证结果。
