A new distribution metric for image segmentation

Romeil Sandhu, Tryphon Georgiou, Allen Tannenbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2008
Subtitle of host publicationImage Processing
StatePublished - May 19 2008
EventMedical Imaging 2008: Image Processing - San Diego, CA, United States
Duration: Feb 17 2008Feb 19 2008

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2008: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA


  • Distributions
  • Geometric active contours
  • Metrics
  • Segmentation


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