Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/81060
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Type: Journal article
Title: Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis
Author: Niu, L.
Qian, M.
Yang, W.
Meng, L.
Xiao, Y.
Wong, K.
Abbott, D.
Liu, X.
Zheng, H.
Citation: PLoS One, 2013; 8(10):1-11
Publisher: Public Library of Science
Issue Date: 2013
ISSN: 1932-6203
1932-6203
Statement of
Responsibility: 
Lili Niu, Ming Qian, Wei Yang, Long Meng, Yang Xiao, Kelvin K. L. Wong, Derek Abbott, Xin Liu, Hairong Zheng
Abstract: There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE−/− mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.
Keywords: Animals; Mice, Knockout; Mice; Carotid Arteries; Disease Models, Animal; Area Under Curve; ROC Curve; Image Processing, Computer-Assisted; Early Diagnosis; Atherosclerosis; Algorithms; Support Vector Machines
Rights: © 2013 Niu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0020132125
DOI: 10.1371/journal.pone.0076880
Appears in Collections:Electrical and Electronic Engineering publications

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