Profile scale-spaces for multiscale image match

Sean Ho, Guido Gerig

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

    Abstract

    We present a novel statistical image-match model for use in Bayesian segmentation, a multiscale extension of image profile models akin to those in Active Shape Models. A spherical-harmonic based 3D shape representation provides a mapping of the object boundary to the sphere S2, and a scale-space for profiles on the sphere defines a scale-space on the object. A key feature is that profiles are not blurred across the object boundary, but only along the boundary. This profile scalespace is sampled in a coarse-to-fine fashion to produce features for the statistical image-match model. A framework for model-building and segmentation has been built, and testing and validation are in progress with a dataset of 70 segmented images of the caudate nucleus.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science
    EditorsC. Barillot, D.R. Haynor, P. Hellier
    Pages176-183
    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

    ASJC Scopus subject areas

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

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