IEEE Trans Image Process. 2014 Sep;23(9):3762-72. doi: 10.1109/TIP.2014.2332761. Epub 2014 Jun 25.
Automatic segmentation of the arterial lumen from ultrasound images is an important task in clinical diagnosis. Carotid artery recognition, the first task in lumen segmentation, should be performed in a fully automated, fast, and reliable way to further facilitate the low-level task of arterial delineation. In this paper, a user-independent, real-time algorithm is introduced for carotid artery localization in longitudinal B-mode ultrasound images. The proposed technique acts directly on the raw image, and exploits basic statistics along with anatomical knowledge. The method's evaluation and parameter value optimization were performed on a threefold cross validation basis. In addition, the introduced algorithm was systematically compared with another algorithm for common carotid artery recognition in B-mode scans, separately for multi-frame and single-frame data. The data sets used included 2,149 images from 100 subjects taken from three different institutions and covering a wide range of possible lumen and surrounding tissue representations. Using the optimized values, the carotid artery was recognized in all the processed images in both multi-frame and single-frame data. Thus, the introduced technique will further reinforce automatic segmentation in longitudinal B-mode ultrasound images.
从超声图像中自动分割动脉管腔是临床诊断中的一项重要任务。颈动脉识别是管腔分割的第一个任务，应该以全自动、快速和可靠的方式进行，以进一步促进动脉描绘的低级任务。本文提出了一种用于纵向 B 模式超声图像中颈动脉定位的用户独立的实时算法。所提出的技术直接作用于原始图像，并利用基本统计数据和解剖学知识。该方法的评估和参数值优化是基于三折交叉验证进行的。此外，所引入的算法还系统地与另一种用于 B 模式扫描中颈总动脉识别的算法进行了比较，分别针对多帧和单帧数据。使用的数据集包括来自三个不同机构的 100 个对象的 2149 张图像，涵盖了广泛的可能的管腔和周围组织表示。使用优化后的参数，所引入的技术在多帧和单帧数据的所有处理图像中都识别出了颈动脉。因此，所提出的技术将进一步加强纵向 B 模式超声图像中的自动分割。
