Automatic identification of vulnerable plaques based on intravascular ultrasound images

被引：1

作者：

Zhang Q. ^{[1
]}

Wang Y.-Y. ^{[2
]}

Ma J.-Y. ^{[3
]}

Qian J.-Y. ^{[3
]}

Shi J. ^{[1
]}

Yan Z.-Z. ^{[1
]}

机构：

[1] School of Communication and Information Engineering, Shanghai University

[2] Department of Electronic Engineering, Fudan University

[3] Department of Cardiology, Zhongshan Hospital of Fudan University

来源：

Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2011年 / 19卷 / 10期

关键词：

Atherosclerotic vulnerable plaque; Feature extraction; Image segmentation; Intravascular ultrasound; Pattern recognition;

D O I：

10.3788/OPE.20111910.2507

中图分类号：

学科分类号：

摘要：

In order to overcome drawbacks in manual identification of vulnerable atherosclerotic plaques, a method for automatic identification of vulnerable plaques is proposed based on computerized analysis of intravascular ultrasound images. First, the Contourlet transform is combined with the Snake model to segment images and detect lumen borders and external elastic membranes. Two categories of new features representing texture and elasticity of plaques are then automatically extracted to quantitate the features of plaques. The texture features consist of first-order statistics and features from the gray-level coocurrence matrix, and the elastic features are extracted from strain tensors estimated by nonrigid image registration. Finally, three types of features are used to design classifiers including Fisher linear discrimination, support vector machines, and generalized relevance learning vector quantization. The experimental results on 124 plaques, consisting of 36 vulnerable and 88 nonvulnerable ones, reveals that 20 morphological features, 24 texture features and 6 elastic features has significant difference (P<0.05) between the two types of plaques. The Support Vector Machine(SVM) outperformes the other two classifiers with the sensitivity, specificity, correct rate, and Youden's index of 91.7%, 97.7%, 96.7%, and 89.4%, respectively. Therefore, the proposed method can automatically and accurately identify vulnerable plaques.

引用

页码：2507 / 2519

页数：12

共 19 条

[1] Naghavi M., Libby P., Falk E., Et al., From vulnerable plaque to vulnerable patient: A call for new definitions and risk assessment strategies: Part I, Circulation, 108, 14, pp. 1664-1672, (2003)
[2] Waxman S., Ishibashi F., Muller J.E., Detection and treatment of vulnerable plaques and vulnerable patients: Novel approaches to prevention of coronary events, Circulation, 114, 22, pp. 2390-2411, (2006)
[3] Gil D., Hernandez A., Rodriguez O., Et al., Statistical strategy for anisotropic adventitia modelling in IVUS, IEEE Transactions on Medical Imaging, 25, 6, pp. 768-778, (2006)
[4] Bovenkamp E.G.P., Dijkstra J., Bosch J.G., Et al., Multi-agent segmentation of IVUS images, Pattern Recognition, 37, 4, pp. 647-663, (2004)
[5] Giannoglou G.D., Chatzizisis Y.S., Koutkias V., Et al., A novel active contour model for fully automated segmentation of intravascular ultrasound images: In vivo validation in human coronary arteries, Computers in Biology and Medicine, 37, 9, pp. 1292-1302, (2007)
[6] Ge J., Chirillo F., Schwedtmann J., Et al., Screening of ruptured plaques in patients with coronary artery disease by intravascular ultrasound, Heart, 81, 6, pp. 621-627, (1999)
[7] Yamagishi M., Terashima M., Awano K., Et al., Morphology of vulnerable coronary plaque: Insights from follow-up of patients examined by intravascular ultrasound before an acute coronary syndrome, Journal of the American College of Cardiology, 35, 1, pp. 106-111, (2000)
[8] Liang Y., Zhu H., Friedman M.H., The correspondence between coronary arterial wall strain and histology in a porcine model of atherosclerosis, Physics in Medicine and Biology, 54, 18, pp. 5625-5641, (2009)
[9] Liu L., Liu W.Y., Chu C.Y., Et al., Fast classification of benign and malignant solitary pulmonary nodules in CT image, Opt. Precision Eng., 17, 8, pp. 2060-2068, (2009)
[10] Wen J.T., Wang B.X., Recognition of floating particles in ampoules by wavelet packet energy spectrum and SVM, Opt. Precision Eng., 17, 11, pp. 2794-2799, (2009)

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