期刊
ULTRASOUND IN MEDICINE AND BIOLOGY
卷 41, 期 7, 页码 2001-2021出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ultrasmedbio.2015.03.022
关键词
Intravascular; Region growing; Ultrasound; Unsupervised clustering
资金
- Guangdong Innovation Research Team Fund for Low-Cost Health-Care Technologies in China
- National High-Tech R & D Program (863 Program) [2012AA02A603]
- Guangzhou Science and Technology Planning Project [2014J4100153]
- Key Lab for Health Informatics of the Chinese Academy of Sciences
- Enhancing Program of Key Laboratories of Shenzhen City [ZDSY20120617113021359]
- Shenzhen government [SGLH20131010163759789]
- Shenzhen Innovation [CXZZ20140901004122087]
- National Natural Science Foundation of China [81101120, 61471243]
An automated framework for detecting lumen and media-adventitia borders in intravascular ultrasound images was developed on the basis of an adaptive region-growing method and an unsupervised clustering method. To demonstrate the capability of the framework, linear regression, Bland-Altman analysis and distance analysis were used to quantitatively investigate the correlation, agreement and spatial distance, respectively, between our detected borders and manually traced borders in 337 intravascular ultrasound images in vivo acquired from six patients. The results of these investigations revealed good correlation (r = 0.99), good agreement (>96.82% of results within the 95% confidence interval) and small average distance errors ( lumen border: 0.08 mm, media-adventitia border: 0.10 mm) between the borders generated by the automated framework and the manual tracing method. The proposed framework was found to be effective in detecting lumen and media-adventitia borders in intravascular ultrasound images, indicating its potential for use in routine studies of vascular disease. (C) 2015 World Federation for Ultrasound in Medicine & Biology.
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