Recent research suggests that the cardiovascular diseases (CVDs), seem to be the foremost cause of mortality among the world populace. Three dimensional (3D) imaging modality such as computed tomography angiography(CTA) is a standard noninvasive imaging modality which has great potentials for the visualization of heart and coronary arteries. This article presents a fully automated method for coronary artery extraction using modified Frangi's vesselness measure and region based segmentation. In this article, grayness and gradient based measures are used while computing Frangi's vesselness measure to improve the extraction of coronary arteries. The obtained vesselness measures are utilized for automatically computing the location of ostia. The locations of ostia are then used as starting seed points in region growing segmentation to extract coronary arteries. Three major coronary arteries, namely the left anterior descending artery (LAD), left circumflex artery (LCX) and right coronary artery (RCA) are segmented using the proposed method and the centerlines are extracted for the main coronary branches. The performance of the proposed method is evaluated using 12 3D CCTA data set. The experimental results reveal that during the calculation of modified Frangi's vesselness measure the proposed method gives improved results. The qualitative results obtained during the segmentation stage are also convincing. The average segmentation accuracy and overlap measure of the proposed method are 97.4% and 77.86%, respectively. Hence, the proposed automated approach can detect and extract coronary arteries in CCTA images with high performance.

中文翻译：

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基于区域的冠状动脉分割，使用改良的Frangi血管测量

最近的研究表明，心血管疾病（CVD）似乎是世界人口中最主要的死亡原因。三维（3D）成像方式，例如计算机断层扫描血管造影（CTA）是一种标准的非侵入性成像方式，具有可视化心脏和冠状动脉的巨大潜力。本文介绍了一种使用改良的Frangi的血管测量和基于区域的分割进行冠状动脉提取的全自动方法。在本文中，在计算Frangi的血管测量值时使用了基于灰度和梯度的测量值，以改善冠状动脉的提取。所获得的血管度测量值用于自动计算口的位置。然后，将口的位置用作区域生长分割中的起始种子点，以提取冠状动脉。使用提出的方法分割左前降支动脉（LAD），左旋支动脉（LCX）和右冠状动脉（RCA）这三个主要冠状动脉，并提取主要冠状动脉分支的中心线。使用12个3D CCTA数据集评估了所提出方法的性能。实验结果表明，在计算改进的Frangi船度测量过程中，所提出的方法给出了改进的结果。在细分阶段获得的定性结果也令人信服。该方法的平均分割精度和重叠度量分别为97.4％和77.86％。因此，