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内中膜厚度:为完全自动化的超声测量方法设定标准。

Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement.

机构信息

Department of Electronics, Politecnico di Torino, Torino, Italy.

出版信息

IEEE Trans Ultrason Ferroelectr Freq Control. 2010 May;57(5):1112-24. doi: 10.1109/TUFFC.2010.1522.

Abstract

The intima-media thickness (IMT) of the common carotid artery is a widely used clinical marker of severe cardiovascular diseases. IMT is usually manually measured on longitudinal B-mode ultrasound images. Many computer-based techniques for IMT measurement have been proposed to overcome the limits of manual segmentation. Most of these, however, require a certain degree of user interaction. In this paper we describe a new, completely automated layer extraction technique (named CALEXia) for the segmentation and IMT measurement of the carotid wall in ultrasound images. CALEXia is based on an integrated approach consisting of feature extraction, line fitting, and classification that enables the automated tracing of the carotid adventitial walls. IMT is then measured by relying on a fuzzy K-means classifier. We tested CALEXia on a database of 200 images. We compared CALEXia?s performance with those of a previously developed methodology that was based on signal analysis (CULEXsa). Three trained operators manually segmented the images and the average profiles were considered as the ground truth. The average error from CALEXia for lumen-intima (LI) and media- adventitia (MA) interface tracings were 1.46 +/- 1.51 pixel (0.091 +/- 0.093 mm) and 0.40 +/- 0.87 pixel (0.025 +/- 0.055 mm), respectively. The corresponding errors for CULEXsa were 0.55 +/- 0.51 pixels (0.035 +/- 0.032 mm) and 0.59 +/- 0.46 pixels (0.037 +/- 0.029 mm). The IMT measurement error was equal to 0.87 +/- 0.56 pixel (0.054 +/- 0.035 mm) for CALEXia and 0.12 +/- 0.14 pixel (0.01 +/- 0.01 mm) for CULEXsa. Thus, CALEXia showed limited performance in segmenting the LI interface, but outperformed CULEXsa in the MA interface and in the number of images correctly processed (190 for CALEXia and 184 for CULEXsa). Based upon two complementary strategies, we anticipate fusing them for further IMT improvements.

摘要

颈总动脉内-中膜厚度(IMT)是广泛应用于严重心血管疾病的临床标志物。IMT 通常在纵向 B 型超声图像上手动测量。已经提出了许多基于计算机的 IMT 测量技术来克服手动分割的限制。然而,其中大多数都需要一定程度的用户交互。在本文中,我们描述了一种新的、完全自动化的颈动脉壁分层提取技术(命名为 CALEXia),用于超声图像中颈动脉壁的分割和 IMT 测量。CALEXia 基于一个集成的方法,包括特征提取、直线拟合和分类,从而实现了对颈动脉外膜的自动追踪。然后,通过依赖模糊 K-均值分类器来测量 IMT。我们在一个包含 200 张图像的数据库上测试了 CALEXia。我们将 CALEXia 的性能与基于信号分析的先前开发的方法(CULEXsa)进行了比较。三名训练有素的操作员手动分割图像,并将平均轮廓视为真实值。CALEXia 对管腔-内膜(LI)和中膜-外膜(MA)界面的平均误差分别为 1.46 +/- 1.51 像素(0.091 +/- 0.093 毫米)和 0.40 +/- 0.87 像素(0.025 +/- 0.055 毫米)。CULEXsa 的相应误差分别为 0.55 +/- 0.51 像素(0.035 +/- 0.032 毫米)和 0.59 +/- 0.46 像素(0.037 +/- 0.029 毫米)。CALEXia 的 IMT 测量误差为 0.87 +/- 0.56 像素(0.054 +/- 0.035 毫米),CULEXsa 的 IMT 测量误差为 0.12 +/- 0.14 像素(0.01 +/- 0.01 毫米)。因此,CALEXia 在分割 LI 界面方面表现不佳,但在 MA 界面和正确处理的图像数量方面优于 CULEXsa(CALEXia 为 190 张,CULEXsa 为 184 张)。基于两种互补策略,我们预计将它们融合以进一步提高 IMT。

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