基于FCM聚类算法与自适应阈值的冠脉钙化分割与量化

doi:10.3969/j.issn.0258-8021.2017.05.006

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Abstract Aiming at the problems of image noise and impressionable threshold in coronary calcium quantification with 3D CTA data, a new method based on fuzzy C-means (FCM) clustering algorithm and self-adapting threshold determination was proposed for automatically segmenting and quantifying the coronary calcium. Firstly, feature vectors are constructed for every voxel in the coronary artery, which contains spatial coordinates and CT value information of the voxel, and a clustering algorithm combined with a self-adapted group number referring to vessel skeleton number is used to divide the coronary artery into different candidate volumes; secondly, a robust threshold determination algorithm based on the histogram is used to extract calcium plaques among those candidate volumes acquired; finally, calcium volume and Agatston score are calculated. Result shows that the method proposed in this study has relatively high sensitivity of 89.5% and specificity of 98.6% in calcium detection in 30 coronary CTA data. The calcium volume and Agatston score calculated automatically show a high correlation with the standard result. The corresponding Pearson correlation coefficient being up to 0.974 and 0.975, respectively, much higher than the 0.523 and 0.501 that calculated by the derivative-based threshold determination (DBTD) method. Experimental results show that this method can be used for coronary calcium segmentation and quantification, and has the characteristics of full automation, robustness and noise immunity.

Key words： CT image processing coronary calcium segmentation and quantification fuzzy C-means(FCM) clustering algorithm calcium score calculation

Received: 13 October 2016

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R318

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	Articles by authors
	Zhao Cong
	Chen Xiaodong
	Zhang Jiachen
	Wang Yi
	Jia Zhongwei
	Chen Xiangzhi

Cite this article:

Zhao Cong,Chen Xiaodong,Zhang Jiachen, et al. Segmentation and Scoring of Coronary Calcium in 3D CTA Data Based on FCM Algorithm and Self-Adapting Threshold Determination[J]. Chinese Journal of Biomedical Engineering, 2017, 36(5): 550-556.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2017.05.006 OR http://cjbme.csbme.org/EN/Y2017/V36/I5/550

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