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W. Eric L. Grimson and John W. Fisher, III. Bose, Biswajit, 1981- Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2010-03-25T15:12:55Z 2010-03-25T15:12:55Z 2009 2009 http://hdl.handle.net/1721.1/53193 526543567 Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 99-104). We present a novel level-set method for representing and detecting open surfaces embedded in three-dimensional image volumes. Open surfaces are two-dimensional manifolds with a one-dimensional boundary lying within a three-dimensional volume. Distinct portions of a closed surface can be modeled as open surfaces, as can very thin volumes with negligible thickness. To detect open surfaces, we propose an interface likelihood model that captures the image appearance along a profile normal to the open surface. This allows statistical modeling of more complex surface-appearance characteristics than just voxel intensities or gradients. Appearance models of the surface are used in the level-set framework in two ways: firstly, to evolve the open surface in the normal direction for the purpose of detecting the location and shape of the surface, and secondly, to evolve the boundary of the open surface in a direction tangential to the surface in order to delineate the extent of the surface. We show that our models are well suited to detecting structures of interest in three-dimensional medical and geological images, and demonstrate their utility on challenging structural magnetic resonance (MR) datasets and seismic-reflection volumes. by Biswajit Bose. Ph.D. 104 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Detecting open surfaces in three dimensions Detecting open surfaces in 3 dimensions Detecting open surfaces in 3D Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="b131b65c-16ec-4fd7-affd-d25ce409ec09"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Detecting open surfaces in three dimensions</Title> <Subtitle>Detecting open surfaces in 3 dimensions</Subtitle> <Subtitle>Detecting open surfaces in 3D</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2009</PublicationDate> <Authors> <Author> <DisplayName>Bose, Biswajit, 1981-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>We present a novel level-set method for representing and detecting open surfaces embedded in three-dimensional image volumes. Open surfaces are two-dimensional manifolds with a one-dimensional boundary lying within a three-dimensional volume. Distinct portions of a closed surface can be modeled as open surfaces, as can very thin volumes with negligible thickness. To detect open surfaces, we propose an interface likelihood model that captures the image appearance along a profile normal to the open surface. This allows statistical modeling of more complex surface-appearance characteristics than just voxel intensities or gradients. Appearance models of the surface are used in the level-set framework in two ways: firstly, to evolve the open surface in the normal direction for the purpose of detecting the location and shape of the surface, and secondly, to evolve the boundary of the open surface in a direction tangential to the surface in order to delineate the extent of the surface. We show that our models are well suited to detecting structures of interest in three-dimensional medical and geological images, and demonstrate their utility on challenging structural magnetic resonance (MR) datasets and seismic-reflection volumes.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>