• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society >A fully automated method using active contours for the evaluation of the intima-media thickness in carotid US images
【24h】

A fully automated method using active contours for the evaluation of the intima-media thickness in carotid US images

机译：使用活动轮廓评估颈动脉US图像中内膜中层厚度的全自动方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The thickness of the intima-media complex (IMC) of the common carotid artery (CCA) wall is important in the evaluation of the risk for the development of atherosclerosis. This paper presents a fully automated algorithm for the segmentation of the IMC. The segmentation of the IMC of the CCA wall is important for the evaluation of the intima media thickness (IMT) on B-mode ultrasound images. The presented algorithm is based on active contours and active contours without edges. It begins with image normalization, followed by speckle removal. The level set formulation of Chan and Vese using random initialization provides a segmentation of the CCA ultrasound (US) images into different distinct regions, one of which corresponds to the carotid wall region above the lumen whilst another corresponds to the carotid wall region below the lumen and includes the IMC. The results of the corresponding segmentation combined with anatomical information provide a very accurate outline of the lumen-intima boundary. This outline serves as an excellent initialization for segmentation of the IMC using parametric active contours. The method lends itself to the development of a fully automated method for the delineation of the IMC. The mean and standard deviation of the thickness of the automatically segmented regions are 0.65 mm +/−0.17 mm and the corresponding values for the ground truth IMT are 0.66 mm +/−0.18 mm. The Wilcoxon rank sum test shows no significant difference.
机译：颈总动脉（CCA）壁的内膜-中膜复合物（IMC）的厚度在评估动脉粥样硬化发展的风险中很重要。本文提出了一种用于IMC分割的全自动算法。 CCA壁的IMC的分割对于B型超声图像上内膜中膜厚度（IMT）的评估很重要。所提出的算法基于活动轮廓和没有边缘的活动轮廓。它从图像归一化开始，然后去除斑点。 Chan和Vese使用随机初始化的水平集公式将CCA超声（US）图像分割为不同的不同区域，其中一个对应于内腔上方的颈动脉壁区域，而另一个对应于内腔下方的颈动脉壁区域。并包括IMC。相应的分割结果与解剖学信息相结合，可提供非常精确的管腔内膜边界轮廓。该轮廓是使用参数活动轮廓对IMC进行分割的出色初始化。该方法有助于为IMC勾画出一种完全自动化的方法。自动分割的区域的厚度的平均值和标准偏差为0.65mm +/- 0.17mm，并且地面真实IMT的相应值为0.66mm +/- 0.18mm。 Wilcoxon秩和检验显示无显着差异。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号