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

    Abstract

    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
    Volume3216
    Issue numberPART 1
    DOIs
    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
    • Computer Science(all)

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