He Xiangjian, Jia Wenjing, Wu Qiang, Hintz Tom
Computer Vision Research Group, University of Technology, Sydney, P.O. Box 123, Broadway 2007, NSW, Australia.
Comput Med Imaging Graph. 2006 Sep-Oct;30(6-7):377-82. doi: 10.1016/j.compmedimag.2006.09.001. Epub 2006 Oct 25.
The most notable characteristic of the heart is its movement. Detection of dynamic information describing cardiac movement such as amplitude, speed and acceleration facilitates interpretation of normal and abnormal function. In recent years, the Omni-directional M-mode Echocardiography System (OMES) has been developed as a process that builds moving information from a sequence of echocardiography image frames. OMES detects cardiac movement through construction and analysis of Position-Time Grey Waveform (PTGW) images on some feature points of the boundaries of the ventricles. Image edge detection plays an important role in determining the feature boundary points and their moving directions as the basis for extraction of PTGW images--Spiral Architecture (SA) has proved efficient for image edge detection. SA is a hexagonal image structure in which an image is represented as a collection of hexagonal pixels. There are two operations called spiral addition and spiral multiplication defined on SA. They correspond to image translation and rotation, respectively. In this paper, we perform ventricle boundary detection based on SA using various defined chain codes. The gradient direction of each boundary point is determined at the same time. PTGW images at each boundary point are obtained through a series of spiral additions according to the directions of boundary points. Unlike the OMES system, our new approach is no longer affected by the translation movement of the heart. As its result, three curves representing the amplitude, speed and acceleration of cardiac movement can be easily drawn from the PTGW images obtained. Our approach is more efficient and accurate than OMES, and our results contain a more robust and complete description of cardiac motion.
心脏最显著的特征是其运动。检测描述心脏运动的动态信息,如振幅、速度和加速度,有助于解读正常和异常功能。近年来,全向M型超声心动图系统(OMES)已被开发出来,它是一个从一系列超声心动图图像帧构建运动信息的过程。OMES通过构建和分析心室边界某些特征点上的位置-时间灰度波形(PTGW)图像来检测心脏运动。图像边缘检测在确定特征边界点及其运动方向方面起着重要作用,这是提取PTGW图像的基础——螺旋结构(SA)已被证明在图像边缘检测方面是有效的。SA是一种六边形图像结构,其中图像被表示为六边形像素的集合。在SA上定义了两种操作,分别称为螺旋加法和螺旋乘法。它们分别对应于图像平移和旋转。在本文中,我们使用各种定义的链码基于SA进行心室边界检测。同时确定每个边界点的梯度方向。根据边界点的方向,通过一系列螺旋加法在每个边界点获得PTGW图像。与OMES系统不同,我们的新方法不再受心脏平移运动的影响。因此,可以很容易地从获得的PTGW图像中绘制出代表心脏运动振幅、速度和加速度的三条曲线。我们的方法比OMES更高效、准确,并且我们的结果包含对心脏运动更稳健和完整的描述。