Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging. (April 2018)

• Record Type: Journal Article
• Authors: Wan, Tao; Shang, Xiaoqing; Yang, Weilin; Chen, Jianhui; Li, Deyu; Qin, Zengchang
• Is Part Of: Computer methods and programs in biomedicine. Volume 157(2018)
• Page Start: 179
• Page End: 190
• Publication Date: 2018-04
• Subjects: Coronary angiography -- Vessel segmentation -- Hessian matrix -- Statistical region merging; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.01.002
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An automated vessel tree segmentation method in angiography images is presented. An adaptive Hessian-based enhancement method improves segmentation performance. A statistical region merging technique segments both principal and thin vessels. Abstract: Background and Objective: Coronary artery segmentation is a fundamental step for a computer-aided diagnosis system to be developed to assist cardiothoracic radiologists in detecting coronary artery diseases. Manual delineation of the vasculature becomes tedious or even impossible with a large number of images acquired in the daily life clinic. A new computerized image-based segmentation method is presented for automatically extracting coronary arteries from angiography images. Methods: A combination of a multiscale-based adaptive Hessian-based enhancement method and a statistical region merging technique provides a simple and effective way to improve the complex vessel structures as well as thin vessel delineation which often missed by other segmentation methods. The methodology was validated on 100 patients who underwent diagnostic coronary angiography. The segmentation performance was assessed via both qualitative and quantitative evaluations. Results: Quantitative evaluation shows that our method is able to identify coronary artery trees with an accuracy of 93% and outperforms other segmentation methods in terms of two widely used segmentation metrics of mean absolute difference and dice similarity coefficient. Conclusions: The comparison to the manual segmentations from three human observers suggests that the presented automated segmentation method is potential to be used in an image-based computerized analysis system for early detection of coronary artery disease.