Dai X, Snyder W E, Bilbro G L, Williams R, Cowan R
Computer Graphics Center at North Carolina State University, Raleigh 27695-7914, USA.
J Digit Imaging. 1998 Feb;11(1):10-20. doi: 10.1007/BF03168721.
We present here a new algorithm for segmentation of nuclear medicine images to detect the left-ventricle (LV) boundary. In this article, other image segmentation techniques, such as edge detection and region growing, are also compared and evaluated. In the edge detection approach, we explored the relationship between the LV boundary characteristics in nuclear medicine images and their radial orientations: we observed that no single brightness function (eg, maximum of first or second derivative) is sufficient to identify the boundary in every direction. In the region growing approach, several criteria, including intensity change, gradient magnitude change, gradient direction change, and running mean differences, were tested. We found that none of these criteria alone was sufficient to successfully detect the LV boundary. Then we proposed a simple but successful region growing method--Contour-Modified Region Growing (CMRG). CMRG is an easy-to-use, robust, and rapid image segmentation procedure. Based on our experiments, this method seems to perform quite well in comparison to other automated methods that we have tested because of its ability to handle the problems of both low signal-to-noise ratios (SNR) as well as low image contrast without any assumptions about the shape of the left ventricle.
我们在此展示一种用于核医学图像分割以检测左心室（LV）边界的新算法。在本文中，还对其他图像分割技术，如边缘检测和区域生长，进行了比较和评估。在边缘检测方法中，我们探究了核医学图像中左心室边界特征与其径向方向之间的关系：我们观察到，没有单一的亮度函数（例如，一阶或二阶导数的最大值）足以在各个方向上识别边界。在区域生长方法中，测试了几个标准，包括强度变化、梯度幅度变化、梯度方向变化和滑动平均差异。我们发现，这些标准单独使用时都不足以成功检测左心室边界。然后我们提出了一种简单但成功的区域生长方法——轮廓修正区域生长（CMRG）。CMRG是一种易于使用、稳健且快速的图像分割程序。基于我们的实验，与我们测试过的其他自动化方法相比，该方法似乎表现良好，因为它能够处理低信噪比（SNR）以及低图像对比度的问题，而无需对左心室的形状做任何假设。
