基于冠脉造影图像血管树分割的血管狭窄自动识别方法

doi:10.3969/j.issn.0258-8021.2019.03.002

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Abstract Automated identification and quantification of the vascular stenoses in coronary angiographical images are essential in a computer-aided diagnosis system, which can improve the diagnosis accuracy, while reducing the labor intensity of doctors in daily clinical practice. We presented a computerized method for automatic detection and grading of vascular stenoses on X-ray angiography in this work, which included two main parts. In the vessel segmentation part, image enhancement was first performed by an improved Frangi Hessian based method, and then the blood vessel regions were segmented using a statistical region merging approach, which could provide good partition of the vascular tree with a complex structure. In the stenosis assessment part, a vessel skeleton was first obtained from a skeletonization method, then vessel diameters were computed based on the boundary points from the vessel tree segmentation, and finally stenosis degree were calculated using ratio between minimum and maximum of the vessel diameters in the vessel stenosis segment. The method was tested on 153 patient studies, and a total of 208 vessel segments were identified, including 84 mild, 42 moderate, and 82 severe stenoses. The method achieved detection accuracy 93.59%, sensitivity 88.76%, specificity 95.58%, and a positive predictive value 90.51%, which suggested the method was able to effectively detect and quantify the artery vessel stenoses, and provided supplement opinion for clinical diagnosis of cardiovascular disease.

Key words： vascular stenosis assessment coronary angiography vessel tree segmentation vascular diameter measurement

Received: 06 July 2018

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R318

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	Articles by authors
	Chen Jianhui
	Zhao Lei
	Li Deyu
	Wan Tao

Cite this article:

Chen Jianhui,Zhao Lei,Li Deyu, et al. Automated Detection and Quantification of Coronary Artery Stenoses Based on Vessel Tree Segmentation in X-Ray Angiography[J]. Chinese Journal of Biomedical Engineering, 2019, 38(3): 266-272.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.03.002 OR http://cjbme.csbme.org/EN/Y2019/V38/I3/266

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