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dc.contributor.authorGao, Yi
dc.contributor.authorSandhu, Romeil
dc.contributor.authorFichtinger, Gabor
dc.contributor.authorTannenbaum, Allen R.
dc.date.accessioned2010-11-05T14:21:39Z
dc.date.available2010-11-05T14:21:39Z
dc.date.issued2010-10
dc.identifier.citationYi Gao, Romeil Sandhu, Gabor Fichtinger, and Allen Robert Tannenbaum, "A Coupled Global Registration and Segmentation Framework With Application to Magnetic Resonance Prostate Imagery," IEEE Transactions on Medical Imaging, Vol. 29, No. 10, October 2010, 1781-1794.en_US
dc.identifier.issn0278-0062
dc.identifier.urihttp://hdl.handle.net/1853/35814
dc.description©2010 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_US
dc.descriptionDOI: 10.1109/TMI.2010.2052065
dc.description.abstractExtracting the prostate from magnetic resonance (MR) imagery is a challenging and important task for medical image analysis and surgical planning. We present in this work a unified shape-based framework to extract the prostate from MR prostate imagery. In many cases, shape-based segmentation is a two-part problem. First, one must properly align a set of training shapes such that any variation in shape is not due to pose. Then segmentation can be performed under the constraint of the learnt shape. However, the general registration task of prostate shapes becomes increasingly difficult due to the large variations in pose and shape in the training sets, and is not readily handled through existing techniques. Thus, the contributions of this paper are twofold. We first explicitly address the registration problem by representing the shapes of a training set as point clouds. In doing so, we are able to exploit the more global aspects of registration via a certain particle filtering based scheme. In addition, once the shapes have been registered, a cost functional is designed to incorporate both the local image statistics as well as the learnt shape prior. We provide experimental results, which include several challenging clinical data sets, to highlight the algorithm’s capability of robustly handling supine/prone prostate registration and the overall segmentation task.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectBiological organsen_US
dc.subjectBiomedical MRIen_US
dc.subjectFeature extractionen_US
dc.subjectImage registrationen_US
dc.subjectImage segmentationen_US
dc.subjectMedical image processingen_US
dc.subjectSurgeryen_US
dc.subjectParticle filteringen_US
dc.titleA Coupled Global Registration and Segmentation Framework With Application to Magnetic Resonance Prostate Imageryen_US
dc.typeArticleen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineering
dc.contributor.corporatenameGeorgia Institute of Technology. Dept. of Biomedical Engineering
dc.contributor.corporatenameEmory University. Dept. of Biomedical Engineering
dc.contributor.corporatenameQueen’s University (Kingston, Ont.). School of Computing
dc.contributor.corporatenameṬekhniyon, Makhon ṭekhnologi le-Yiśraʼel
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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