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

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

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

    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)
    Title of host publicationLecture Notes in Computer Science
    EditorsC. Barillot, D.R. Haynor, P. Hellier
    Pages95-102
    Number of pages8
    Volume3216
    EditionPART 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

    Other

    OtherMedical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings
    CountryFrance
    CitySaint-Malo
    Period9/26/049/29/04

    Fingerprint

    Atlas
    Multi-class
    Template
    Large Deformation
    Image analysis
    Probability density function
    Brain
    Medical Image Analysis
    Kullback-Leibler Divergence
    Density Function
    Registration
    Linearly
    Class
    Arbitrary
    Energy

    ASJC Scopus subject areas

    • Computer Science (miscellaneous)
    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Lorenzen, P., Davis, B., Gerig, G., Bullitt, E., & Joshi, S. (2004). Multi-class posterior atlas formation via unbiased Kullback-Leibler template estimation. In C. Barillot, D. R. Haynor, & P. Hellier (Eds.), Lecture Notes in Computer Science (PART 1 ed., Vol. 3216, pp. 95-102)

    Multi-class posterior atlas formation via unbiased Kullback-Leibler template estimation. / Lorenzen, Peter; Davis, Brad; Gerig, Guido; Bullitt, Elizabeth; Joshi, Sarang.

    Lecture Notes in Computer Science. ed. / C. Barillot; D.R. Haynor; P. Hellier. Vol. 3216 PART 1. ed. 2004. p. 95-102.

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

    Lorenzen, P, Davis, B, Gerig, G, Bullitt, E & Joshi, S 2004, Multi-class posterior atlas formation via unbiased Kullback-Leibler template estimation. in C Barillot, DR Haynor & P Hellier (eds), Lecture Notes in Computer Science. PART 1 edn, vol. 3216, pp. 95-102, Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings, Saint-Malo, France, 9/26/04.
    Lorenzen P, Davis B, Gerig G, Bullitt E, Joshi S. Multi-class posterior atlas formation via unbiased Kullback-Leibler template estimation. In Barillot C, Haynor DR, Hellier P, editors, Lecture Notes in Computer Science. PART 1 ed. Vol. 3216. 2004. p. 95-102
    Lorenzen, Peter ; Davis, Brad ; Gerig, Guido ; Bullitt, Elizabeth ; Joshi, Sarang. / Multi-class posterior atlas formation via unbiased Kullback-Leibler template estimation. Lecture Notes in Computer Science. editor / C. Barillot ; D.R. Haynor ; P. Hellier. Vol. 3216 PART 1. ed. 2004. pp. 95-102
    @inproceedings{a951b308460147e088b513949b2e6f38,
    title = "Multi-class posterior atlas formation via unbiased Kullback-Leibler template estimation",
    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.",
    author = "Peter Lorenzen and Brad Davis and Guido Gerig and Elizabeth Bullitt and Sarang Joshi",
    year = "2004",
    language = "English (US)",
    volume = "3216",
    pages = "95--102",
    editor = "C. Barillot and D.R. Haynor and P. Hellier",
    booktitle = "Lecture Notes in Computer Science",
    edition = "PART 1",

    }

    TY - GEN

    T1 - Multi-class posterior atlas formation via unbiased Kullback-Leibler template estimation

    AU - Lorenzen, Peter

    AU - Davis, Brad

    AU - Gerig, Guido

    AU - Bullitt, Elizabeth

    AU - Joshi, Sarang

    PY - 2004

    Y1 - 2004

    N2 - 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.

    AB - 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.

    UR - http://www.scopus.com/inward/record.url?scp=20344375203&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=20344375203&partnerID=8YFLogxK

    M3 - Conference contribution

    AN - SCOPUS:20344375203

    VL - 3216

    SP - 95

    EP - 102

    BT - Lecture Notes in Computer Science

    A2 - Barillot, C.

    A2 - Haynor, D.R.

    A2 - Hellier, P.

    ER -