Texture Analysis and Classification of Vascular Plaque from Optical Coherence Tomography Images

dc.contributor.authorPrakash, Ammu
dc.contributor.examiningcommitteeMajor, Arkady (Electrical and Computer Engineering) Hewko, Mark (Biosystems Engineering)en_US
dc.contributor.supervisorSherif, Sherif (Electrical and Computer Engineering)en_US
dc.date.accessioned2013-04-04T14:19:13Z
dc.date.available2013-04-04T14:19:13Z
dc.date.issued2013-04-04
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractThe ability to detect atherosclerotic plaque from optical coherence tomography (OCT) images by visual inspection is usually limited. We developed a texture based segmentation method using supervised and unsupervised classification to detect atherosclerotic plaque from OCT images without any reliance on visual inspection. Our Supervised method involves extraction of statistical textural features using the Spatial Gray Level Dependence Matrix (SGLDM) method, feature extraction and feature selection method, and application on supervised algorithm (K-nn). Our second method is based on unsupervised classification involves extraction of statistical textural features using the SGLDM method, application of an unsupervised clustering algorithm (K-means) on these features, and mapping of the segmented regions of features back to the actual image. We verified our results by visually comparing them to photographs of the vascular tissue with atherosclerotic plaque that we used to generate our OCT images. Our method could be potentially used in clinical cardiovascular OCT imaging.en_US
dc.description.noteMay 2013en_US
dc.identifier.urihttp://hdl.handle.net/1993/18338
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectBiomedicalen_US
dc.titleTexture Analysis and Classification of Vascular Plaque from Optical Coherence Tomography Imagesen_US
dc.typemaster thesisen_US
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