Automated Detection Framework of the Calcified Plaque with Acoustic Shadowing in IVUS Images

• Published in: PloS one Vol. 9; no. 11; p. e109997
• Main Authors: Gao, Zhifan, Guo, Wei, Liu, Xin, Huang, Wenhua, Zhang, Heye, Tan, Ning, Hau, William Kongto, Zhang, Yuan-Ting, Liu, Huafeng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.11.2014; Public Library of Science (PLoS)
• Subjects: Acoustics; Algorithms; Artificial intelligence; Atherosclerosis; Biomedical engineering; Calcification; Calcinosis - diagnostic imaging; Calcium; Cardiology; Cardiovascular disease; Confidence intervals; Coronary artery; Coronary Vessels - diagnostic imaging; Correlation; Correlation analysis; Cybernetics; Doctors; Engineering; Engineering and Technology; Heart attacks; Humans; Image acquisition; Image detection; Image Interpretation, Computer-Assisted - methods; Imaging systems; Laboratories; Markov chains; Measurement methods; Medical imaging; Medical personnel; Medical treatment; Medicine and Health Sciences; Partial differential equations; Physicians; Plaque, Atherosclerotic - diagnostic imaging; Plaques; Quantitative analysis; Regression analysis; Research and Analysis Methods; Sensitivity; Sensitivity analysis; Statistical analysis; Ultrasonic imaging; Ultrasonic testing; Ultrasonography; Ultrasound; Hong Kong China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0109997

Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.