Araki Tadashi, Kumar P Krishna, Suri Harman S, Ikeda Nobutaka, Gupta Ajay, Saba Luca, Rajan Jeny, Lavra Francesco, Sharma Aditya M, Shafique Shoaib, Nicolaides Andrew, Laird John R, Suri Jasjit S
Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan.
Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India.
J Med Syst. 2016 Jul;40(7):182. doi: 10.1007/s10916-016-0543-0. Epub 2016 Jun 14.
The degree of stenosis in the carotid artery can be predicted using automated carotid lumen diameter (LD) measured from B-mode ultrasound images. Systolic velocity-based methods for measurement of LD are subjective. With the advancement of high resolution imaging, image-based methods have started to emerge. However, they require robust image analysis for accurate LD measurement. This paper presents two different algorithms for automated segmentation of the lumen borders in carotid ultrasound images. Both algorithms are modeled as a two stage process. Stage one consists of a global-based model using scale-space framework for the extraction of the region of interest. This stage is common to both algorithms. Stage two is modeled using a local-based strategy that extracts the lumen interfaces. At this stage, the algorithm-1 is modeled as a region-based strategy using a classification framework, whereas the algorithm-2 is modeled as a boundary-based approach that uses the level set framework. Two sets of databases (DB), Japan DB (JDB) (202 patients, 404 images) and Hong Kong DB (HKDB) (50 patients, 300 images) were used in this study. Two trained neuroradiologists performed manual LD tracings. The mean automated LD measured was 6.35 ± 0.95 mm for JDB and 6.20 ± 1.35 mm for HKDB. The precision-of-merit was: 97.4 % and 98.0 % w.r.t to two manual tracings for JDB and 99.7 % and 97.9 % w.r.t to two manual tracings for HKDB. Statistical tests such as ANOVA, Chi-Squared, T-test, and Mann-Whitney test were conducted to show the stability and reliability of the automated techniques.
利用从B模式超声图像测量得到的颈动脉管腔直径（LD），可以预测颈动脉的狭窄程度。基于收缩期速度的LD测量方法具有主观性。随着高分辨率成像技术的发展，基于图像的方法开始出现。然而，它们需要强大的图像分析来准确测量LD。本文提出了两种不同的算法，用于自动分割颈动脉超声图像中的管腔边界。两种算法都被建模为一个两阶段过程。第一阶段由一个基于全局的模型组成，该模型使用尺度空间框架来提取感兴趣区域。这一阶段是两种算法共有的。第二阶段使用基于局部的策略进行建模，该策略提取管腔界面。在这一阶段，算法1被建模为使用分类框架的基于区域的策略，而算法2被建模为使用水平集框架的基于边界的方法。本研究使用了两组数据库（DB），即日本数据库（JDB）（202例患者，404幅图像）和香港数据库（HKDB）（50例患者，300幅图像）。两名经过培训的神经放射科医生进行了手动LD追踪。JDB测量的自动LD平均值为6.35±0.95毫米，HKDB为6.20±1.35毫米。相对于JDB的两次手动追踪，优点精度分别为97.4%和98.0%；相对于HKDB的两次手动追踪，优点精度分别为99.7%和97.9%。进行了方差分析、卡方检验、t检验和曼-惠特尼检验等统计测试，以显示自动技术的稳定性和可靠性。
