Propagating distributions for segmentation of brain atlas

T. Riklin-Raviv, N. Soechen, N. Kiryati, N. Ben-Zadok, S. Gefen, L. Bertand, J. Nissanov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

We present a novel method for segmentation of anatomical structures in histological data. Segmentation is carried out slice-by-slice where the successful segmentation of one section provides a prior for the subsequent one. Intensities and spatial locations of the region of interest and the background are modeled by three-dimensional Gaussian mixtures. This information adaptively propagates across the sections. Segmentation is inferred by minimizing a cost functional that enforces the compatibility of the partitions with the corresponding models together with the alignment of the boundaries with the image gradients. The algorithm is demonstrated on histological images of mouse brain. The segmentation results compare well with manual segmentation.

Original languageEnglish
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages1304-1307
Number of pages4
DOIs
StatePublished - 27 Nov 2007
Externally publishedYes
Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
Duration: 12 Apr 200715 Apr 2007

Publication series

Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

Conference

Conference2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Country/TerritoryUnited States
CityArlington, VA
Period12/04/0715/04/07

Keywords

  • Brain atlas
  • Gaussian mixture model
  • Level sets
  • Segmentation

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