Detection of atherosclerotic plaque from optical coherence tomography images using texture-based segmentation

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DOIResolve DOI: http://doi.org/10.17691/stm2015.7.1.03
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TypeArticle
Journal titleSovremennye Tehnologii v Medicine
ISSN2076-4243
Volume7
Issue1
Pages2128; # of pages: 8
Subjectatherosclerotic plaque; automatic thresholding technique; binary image; controlled study; histogram; image analysis; image clustering algorithm; image processing; image quality; interferometer; optical coherence tomography; spatial dependence matrix; spatial gray level dependent matrix method; swept source optical coherence tomography; texture segmentation; vascular tissue; Watanabe heritable hyperlipidemic rabbit
AbstractDetection of atherosclerotic plaque from optical coherence tomography (OCT) images by visual inspection is difficult. We developed a texture based segmentation method to identify atherosclerotic plaque automatically from OCT images without any reliance on visual inspection. Our method involves extraction of texture statistical features (spatial gray level dependence matrix method), application of an unsupervised clustering algorithm (K-means) on these features, and mapping of the clustered regions: background, plaque, vascular tissue and an OCT degraded signal region in feature-space, back to the actual image. We verified the validity of our results by visual comparison to photographs of the vascular tissue with atherosclerotic plaque that were used to generate our OCT images. Our method could be potentially used in clinical studies in OCT imaging of atherosclerotic plaque.
Publication date
PublisherNizhny Novgorod State Medical Academy
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); Medical Devices
Peer reviewedYes
NPARC number21275787
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Record identifier7ee506bd-dd14-4738-8a0f-b78742c7cd19
Record created2015-07-14
Record modified2016-05-09
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