title: Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis.
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.
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Lili
authors.initials: L
authors.lastname: Niu
authors.forename: Ming
authors.initials: M
authors.lastname: Qian
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.forename: Long
authors.initials: L
authors.lastname: Meng
authors.forename: Yang
authors.initials: Y
authors.lastname: Xiao
authors.forename: Kelvin K L
authors.initials: KK
authors.lastname: Wong
authors.forename: Derek
authors.initials: D
authors.lastname: Abbott
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.forename: Hairong
authors.initials: H
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D050197:Atherosclerosis / Q000000981:diagnostic imaging* / Q000473:pathology*; D002339:Carotid Arteries / Q000000981:diagnostic imaging / Q000473:pathology; D004195:Disease Models, Animal; D042241:Early Diagnosis; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D018345:Mice, Knockout; D012372:ROC Curve; D060388:Support Vector Machine; D014463:Ultrasonography