Department of Computer Science, University of Cyprus, Nicosia 1678, Cyprus.
IEEE Trans Biomed Eng. 2012 Nov;59(11):3060-9. doi: 10.1109/TBME.2012.2214387. Epub 2012 Aug 21.
The segmentation of the intima-media complex (IMC) of the common carotid artery (CCA) wall is important for the evaluation of the intima media thickness (IMT) on B-mode ultrasound (US) images. The IMT is considered an important marker in the evaluation of the risk for the development of atherosclerosis. The fully automated segmentation algorithm presented in this article is based on active contours and active contours without edges and incorporates anatomical information to achieve accurate segmentation. The level set formulation by Chan and Vese using random initialization provides a segmentation of the CCA US images into different distinct regions, one of which corresponds to the carotid wall region below the lumen and includes the far wall IMC. The segmented regions are used to automatically achieve image normalization, which is followed by speckle removal. The resulting smoothed lumen-intima boundary combined with anatomical information provide an excellent initialization for parametric active contours that provide the final IMC segmentation. The algorithm is extensively evaluated on 100 different cases with ground truth (GT) segmentation available from two expert clinicians. The GT mean IMT value is 0.6679 mm +/ - 0.1350 mm and the corresponding automatically segmented (AS) mean IMT value is 0.6054 mm +/ - 0.1464 mm. The mean absolute difference between the GT IMT and the IMT evaluated from from the AS region is 0.095 mm +/ - 0.0615 mm. The polyline distance is 0.096 mm +/ - 0.034 mm while the Hausdorff distance is 0.176 mm +/ - 0.047 mm. The algorithm compares favorably to both automatic and semiautomatic methods presented in the literature.
颈总动脉(CCA)管壁的内中膜复合体(IMC)的分割对于 B 型超声(US)图像上的内中膜厚度(IMT)评估很重要。IMT 被认为是评估动脉粥样硬化发展风险的重要标志物。本文提出的全自动分割算法基于主动轮廓和无边缘的主动轮廓,并结合解剖学信息来实现精确分割。Chan 和 Vese 使用随机初始化的水平集公式提供了 CCA US 图像的分割,将其分为不同的明显区域,其中一个区域对应于管腔下方的颈动脉壁区域,并包括远壁 IMC。分割的区域用于自动实现图像归一化,然后去除斑点。平滑的管腔-内膜边界与解剖学信息相结合,为参数主动轮廓提供了极好的初始化,从而提供最终的 IMC 分割。该算法在 100 个具有来自两位专家临床医生的真实分割(GT)的不同病例上进行了广泛评估。GT 的平均 IMT 值为 0.6679mm+/ - 0.1350mm,相应的自动分割(AS)平均 IMT 值为 0.6054mm+/ - 0.1464mm。GT 的 IMT 与从 AS 区域评估的 IMT 的平均绝对差值为 0.095mm+/ - 0.0615mm。折线距离为 0.096mm+/ - 0.034mm,而 Hausdorff 距离为 0.176mm+/ - 0.047mm。该算法与文献中提出的自动和半自动方法相比具有优势。