Atherosclerotic Plaque Pathological Analysis by Unsupervised K-Means Clustering.

	Atherosclerotic Plaque Pathological Analysis by Unsupervised K-Means Clustering.
	Zhang, Yongtao; Liu, Xin; Yue, Guanghua; Su, Haijun; Zhang, Peng-Fei; Feng, Jianqin
刊名	IEEE ACCESS
	2018
文献子类	期刊论文
英文摘要	This paper introduced a high-throughput pathological analysis algorithm by using of unsupervised K-means clustering principle and lab color space. The accuracy of this algorithm was verified by comparing with well-established commercially available software. For each type of pathological staining special for atherosclerotic plaque components analysis, accurate pathological analysis results could be obtained by selecting the appropriate cluster classification number (usually 3 to 5, but not limited to 3 to 5). Bland-Altman and linear regression analysis further confirmed that the self-developed algorithm correlated well with the well-established software (correlation coefficient R-2 ranged from 0.72 to 0.99). Moreover, the intra- and inter- observer coefficient of variation were relatively minor, indicating very good reproducibility. So we draw a conclusion that the self-developed algorithm could reduce the human interference factors, improve the efficiency, and be suitable for a large number of analyses of atherosclerotic pathology.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/14325]
专题	深圳先进技术研究院_医工所
推荐引用方式 GB/T 7714	Zhang, Yongtao,Liu, Xin,Yue, Guanghua,et al. Atherosclerotic Plaque Pathological Analysis by Unsupervised K-Means Clustering.[J]. IEEE ACCESS,2018.
APA	Zhang, Yongtao,Liu, Xin,Yue, Guanghua,Su, Haijun,Zhang, Peng-Fei,&Feng, Jianqin.(2018).Atherosclerotic Plaque Pathological Analysis by Unsupervised K-Means Clustering..IEEE ACCESS.
MLA	Zhang, Yongtao,et al."Atherosclerotic Plaque Pathological Analysis by Unsupervised K-Means Clustering.".IEEE ACCESS (2018).