Title:
A New Distribution Metric for Image Segmentation
A New Distribution Metric for Image Segmentation
| dc.contributor.author | Sandhu, Romeil | |
| dc.contributor.author | Georgiou, Tryphon T. | |
| dc.contributor.author | Tannenbaum, Allen R. | |
| dc.contributor.corporatename | University of Minnesota. Dept. of Electrical and Computer Engineering | |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Electrical and Computer Engineering | |
| dc.date.accessioned | 2009-06-23T14:27:12Z | |
| dc.date.available | 2009-06-23T14:27:12Z | |
| dc.date.issued | 2008-02 | |
| dc.description | ©2008 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.769010 | en |
| dc.description | Presented at Medical Imaging 2008: Image Processing, February 17-19, 2008, San Diego, CA, USA. | |
| dc.description | DOI: 10.1117/12.769010 | |
| dc.description.abstract | In this paper, we present a new distribution metric for image segmentation that arises as a result in prediction theory. Forming a natural geodesic, our metric quantifies “distance” for two density functionals as the standard deviation of the difference between logarithms of those distributions. Using level set methods, we incorporate an energy model based on the metric into the Geometric Active Contour framework. Moreover, we briefly provide a theoretical comparison between the popular Fisher Information metric, from which the Bhattacharyya distance originates, with the newly proposed similarity metric. In doing so, we demonstrate that segmentation results are directly impacted by the type of metric used. Specifically, we qualitatively compare the Bhattacharyya distance and our algorithm on the Kaposi Sarcoma, a pathology that infects the skin. We also demonstrate the algorithm on several challenging medical images, which further ensure the viability of the metric in the context of image segmentation. | en |
| dc.identifier.citation | Romeil Sandhua, Tryphon Georgiou, and Allen Tannenbaum, "A New Distribution Metric for Image Segmentation," Medical Imaging 2008: Image Processing, Joseph M. Reinhardt, Josien P. W. Pluim, Editors, Proc. SPIE, Vol. 6914, 691404 (2008) | en |
| dc.identifier.issn | 0277-786X | |
| dc.identifier.uri | http://hdl.handle.net/1853/28595 | |
| dc.language.iso | en_US | en |
| dc.publisher | Georgia Institute of Technology | en |
| dc.publisher.original | Society of Photo-Optical Instrumentation Engineers | |
| dc.subject | Geometric active contours | en |
| dc.subject | Distributions | en |
| dc.subject | Segmentation | en |
| dc.subject | Metrics | en |
| dc.title | A New Distribution Metric for Image Segmentation | en |
| dc.type | Text | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Wallace H. Coulter Department of Biomedical Engineering | |
| relation.isOrgUnitOfPublication | da59be3c-3d0a-41da-91b9-ebe2ecc83b66 |