Biomedical Engineering Laboratory, University of Tlemcen Algeria, Tlemcen, Algeria.
J Digit Imaging. 2012 Apr;25(2):294-306. doi: 10.1007/s10278-011-9404-z.
Segmentation of the left ventricle in MRI images is a task with important diagnostic power. Currently, the evaluation of cardiac function involves the global measurement of volumes and ejection fraction. This evaluation requires the segmentation of the left ventricle contour. In this paper, we propose a new method for automatic detection of the endocardial border in cardiac magnetic resonance images, by using a level set segmentation-based approach. To initialize this level set segmentation algorithm, we propose to threshold the original image and to use the binary image obtained as initial mask for the level set segmentation method. For the localization of the left ventricular cavity, used to pose the initial binary mask, we propose an automatic approach to detect this spatial position by the evaluation of a metric indicating object's roundness. The segmentation process starts by the initialization of the level set algorithm and ended up through a level set segmentation. The validation process is achieved by comparing the segmentation results, obtained by the automated proposed segmentation process, to manual contours traced by tow experts. The database used was containing one automated and two manual segmentations for each sequence of images. This comparison showed good results with an overall average similarity area of 97.89%.
MRI 图像中的左心室分割是一项具有重要诊断能力的任务。目前，心脏功能的评估涉及到容积和射血分数的全局测量。这种评估需要分割左心室轮廓。在本文中，我们提出了一种新的方法，用于自动检测心脏磁共振图像中的心内膜边界，使用基于水平集分割的方法。为了初始化这个水平集分割算法，我们提出对原始图像进行阈值处理，并将得到的二值图像用作水平集分割方法的初始掩模。对于左心室腔的定位，用于构成初始二进制掩模，我们提出了一种自动方法，通过评估指示物体圆形度的度量来检测这个空间位置。分割过程通过初始化水平集算法开始，并通过水平集分割结束。通过将自动分割过程得到的分割结果与两位专家手动追踪的轮廓进行比较来完成验证过程。使用的数据库包含每个图像序列的一个自动和两个手动分割。这种比较显示了良好的结果，整体平均相似度面积为 97.89%。
