Multi-class posterior atlas formation via unbiased Kullback-Leibler template estimation

Peter Lorenzen, Brad Davis, Guido Gerig, Elizabeth Bullitt, Sarang Joshi

Research output: Contribution to journalConference articlepeer-review


Many medical image analysis problems that involve multi-modal images lend themselves to solutions that involve class posterior density function images. This paper presents a method for large deformation exemplar class posterior density template estimation. This method generates a representative anatomical template from an arbitrary number of topologically similar multi-modal image sets using large deformation minimum Kullback-Leibler divergence registration. The template that we generate is the class posterior that requires the least amount of deformation energy to be transformed into every class posterior density (each characterizing a multi-modal image set). This method is computationally practical; computation times grows linearly with the number of image sets. Template estimation results are presented for a set of five 3D class posterior images representing structures of the human brain.

Original languageEnglish (US)
Pages (from-to)95-102
Number of pages8
JournalLecture Notes in Computer Science
Issue numberPART 1
StatePublished - 2004
EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France
Duration: Sep 26 2004Sep 29 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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