LUMEN SEGMENTATION IN INTRAVASCULAR ULTRASOUND DATA

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dc.contributor.advisorKakadiaris, Ioannis A.
dc.contributor.committeeMemberShah, Shishir Kirit
dc.contributor.committeeMemberVilalta, Ricardo
dc.contributor.committeeMemberPapadakis, Emanuel I.
dc.contributor.committeeMemberBiros, George
dc.creatorMendizabal Ruiz, Eduardo 1980-
dc.date.accessioned2014-12-18T15:01:22Z
dc.date.available2014-12-18T15:01:22Z
dc.date.createdDecember 2012
dc.date.issued2012-12
dc.date.updated2014-12-18T15:01:22Z
dc.description.abstractIntravascular ultrasound (IVUS) is a catheter-based medical imaging technique that is capable of producing high resolution cross-sectional images of interior of blood vessels and it is currently the gold standard technique for the study of the characteristics of the atherosclerotic plaques. Segmentation of IVUS images refers to the delineation of the lumen/ intima and media/adventitia interfaces of the vessel. This process is necessary for assessing morphological characteristics of the vessel such as lumen diameter, minimum lumen cross-section area, and total atheroma volume. This information is crucial for making decisions such as whether a stent is needed to restore blood flow in an artery and to determine the characteristics of the stent. Other applications of IVUS include the study of mechanical properties of the vessel wall, characteristics of the plaque, and 3D reconstruction of the vessel. Segmentation of IVUS images may be performed manually by an observer. However, depending on the type of analysis, the number of frames to be segmented can range from a few frames to hundreds of frames. In this dissertation, we present a unified computational method for the semi-automatic segmentation of the luminal/wall interface in IVUS data. The method can be used with either B-mode or RF-data and it is based on the deformation of a curve by optimizing a probabilistic cost function. The main contribution is the development of a physicsbased inverse method for the segmentation of the lumen employing the IVUS RF data as compared with previous method which employs the B-mode reconstruction. Experimental results demonstrate the robustness and accuracy of the method. These results pave the way for the automation of the analysis of contrast-enhanced IVUS images to assess extraluminal perfusion.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/823
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectUltrasound
dc.subjectIntravascular Ultrasound
dc.subjectCardiovascular diseases
dc.subjectSegmentation
dc.subject.lcshComputer science
dc.titleLUMEN SEGMENTATION IN INTRAVASCULAR ULTRASOUND DATA
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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