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dc.contributor.authorYang, Yan
dc.contributor.authorTannenbaum, Allen R.
dc.contributor.authorGiddens, Don P.
dc.date.accessioned2009-11-13T17:27:29Z
dc.date.available2009-11-13T17:27:29Z
dc.date.issued2004-09
dc.identifier.citationYan Yang, Allen Tannenbaum, and Don Giddens. "Knowledge-Based 3D Segmentation and Reconstruction of Coronary Arteries Using CT Images," Proceedings of the 26th Annual International Conference of the IEEE EMBS, 1664-1666, 2004.en
dc.identifier.isbn0-7803-8439-3
dc.identifier.urihttp://hdl.handle.net/1853/31174
dc.description©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en
dc.descriptionDOI: 10.1109/IEMBS.2004.1403502
dc.description.abstractAn approach for the 3D segmentation and reconstruction of human left coronary arteries using angio-CT images is presented in This work. Each voxel in the 3D dataset is assumed to belong to one of the three homogeneous regions: blood, myocardium, and lung. A priori knowledge of the regions is introduced via Bayes' rule. Posterior probabilities obtained using Bayes' rule are anisotropically smoothed, and the 3D segmentation is obtained via MAP classifications of the smoothed posteriors. An active contour model is then applied to extract the coronary arteries from the rest of the volumetric data with subvoxel accuracy. The geometric model of the left coronary arteries obtained in this work may be used to provide accurate boundary conditions for hemodynamic simulations, or to provide objective measurements of clinically relevant parameters such as lumen sizes in a 3D sense.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectActive contoursen
dc.subjectBayes methodsen
dc.subjectBlood-vessels
dc.subjectComputed tomography
dc.subjectImage classification
dc.subjectImage reconstruction
dc.subjectImage segmentation
dc.subjectMedical image processing
dc.subjectPhysiological models
dc.subjectSmoothing methods
dc.titleKnowledge-based 3D segmentation and reconstruction of coronary arteries using CT imagesen
dc.typeProceedingsen
dc.contributor.corporatenameGeorgia Institute of Technology. Dept. of Biomedical Engineering
dc.contributor.corporatenameEmory University. Dept. of Biomedical Engineering
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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