Title:
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
| dc.contributor.author | Dambreville, Samuel | |
| dc.contributor.author | Rathi, Yogesh | |
| dc.contributor.author | Tannenbaum, Allen R. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Dept. of Biomedical Engineering | |
| dc.contributor.corporatename | Emory University. Dept. of Biomedical Engineering | |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Electrical and Computer Engineering | |
| dc.contributor.corporatename | Harvard Medical School. Psychiatry Neuroimaging Lab | |
| dc.date.accessioned | 2009-05-19T14:49:42Z | |
| dc.date.available | 2009-05-19T14:49:42Z | |
| dc.date.issued | 2008-08 | |
| dc.description | ©2008 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.description | DOI: 10.1109/TPAMI.2007.70774 | |
| dc.description.abstract | Segmentation involves separating an object from the background in a given image. The use of image information alone often leads to poor segmentation results due to the presence of noise, clutter or occlusion. The introduction of shape priors in the geometric active contour (GAC) framework has proved to be an effective way to ameliorate some of these problems. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, using level-sets. Following the work of Leventon et al., we propose to revisit the use of PCA to introduce prior knowledge about shapes in a more robust manner. We utilize kernel PCA (KPCA) and show that this method outperforms linear PCA by allowing only those shapes that are close enough to the training data. In our segmentation framework, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description permits to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, occlusions, or smearing. | en |
| dc.identifier.citation | Samuel Dambreville, Yogesh Rathi, and Allen Tannenbaum, "A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 8, August 2008, 1385-1399 | en |
| dc.identifier.issn | 0162-8828 | |
| dc.identifier.uri | http://hdl.handle.net/1853/27932 | |
| dc.language.iso | en_US | en |
| dc.publisher | Georgia Institute of Technology | en |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | |
| dc.subject | Kernel methods | en |
| dc.subject | Shape priors | en |
| dc.subject | Active contours | en |
| dc.subject | Principal component analysis | en |
| dc.subject | Level sets | en |
| dc.title | A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors | en |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Wallace H. Coulter Department of Biomedical Engineering | |
| relation.isOrgUnitOfPublication | da59be3c-3d0a-41da-91b9-ebe2ecc83b66 |