Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis

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dc.contributor.authorNiu, L.
dc.contributor.authorQian, M.
dc.contributor.authorYang, W.
dc.contributor.authorMeng, L.
dc.contributor.authorXiao, Y.
dc.contributor.authorWong, K.
dc.contributor.authorAbbott, D.
dc.contributor.authorLiu, X.
dc.contributor.authorZheng, H.
dc.contributor.editorYang, X.
dc.date.issued2013
dc.description.abstractThere 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.
dc.description.statementofresponsibilityLili Niu, Ming Qian, Wei Yang, Long Meng, Yang Xiao, Kelvin K. L. Wong, Derek Abbott, Xin Liu, Hairong Zheng
dc.identifier.citationPLoS One, 2013; 8(10):1-11
dc.identifier.doi10.1371/journal.pone.0076880
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.orcidAbbott, D. [0000-0002-0945-2674]
dc.identifier.urihttp://hdl.handle.net/2440/81060
dc.language.isoen
dc.publisherPublic Library of Science
dc.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.
dc.source.urihttps://doi.org/10.1371/journal.pone.0076880
dc.subjectAnimals
dc.subjectMice, Knockout
dc.subjectMice
dc.subjectCarotid Arteries
dc.subjectDisease Models, Animal
dc.subjectArea Under Curve
dc.subjectROC Curve
dc.subjectImage Processing, Computer-Assisted
dc.subjectEarly Diagnosis
dc.subjectAtherosclerosis
dc.subjectAlgorithms
dc.subjectSupport Vector Machines
dc.titleSurface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis
dc.typeJournal article
pubs.publication-statusPublished

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