Multiscale medial shape-based analysis of image objects

Stephen M. Pizer, Guido Gerig, Sarang Joshi, Stephen R. Aylward

    Research output: Contribution to journalArticlepeer-review


    Medial representation of a three-dimensional (3-D) object or an ensemble of 3-D objects involves capturing the object interior as a locus of medial atoms, each atom being two vectors of equal length joined at the tail at the medial point. Medial representation has a variety of beneficial properties, among the most important of which are 1) its inherent geometry, provides an object-intrinsic coordinate system and thus provides correspondence between instances of the object in and near the object(s); 2) it captures the object interior and is, thus, very suitable for deformation; and 3) it provides the basis for an intuitive object-based multiscale sequence leading to efficiency of segmentation algorithms and trainability of statistical characterizations with limited training sets. As a result of these properties, medial representation is particularly suitable for the following image analysis tasks; how each operates will be described and will be illustrated by results: 1) segmentation of objects and object complexes via deformable models; 2) segmentation of tubular trees, e.g., of blood vessels, by following height ridges of measures of fit of medial atoms to target images; 3) object-based image registration via medial loci of such blood vessel trees; 4) statistical characterization of shape differences between control and pathological classes of structures. These analysis tasks are made possible by a new form of medial representation called m-reps, which is described.

    Original languageEnglish (US)
    Pages (from-to)1670-1679
    Number of pages10
    JournalProceedings of the IEEE
    Issue number10
    StatePublished - Oct 2003


    • Discrimination
    • Medial
    • Medical image
    • Multiscale
    • Object
    • Registration
    • Segmentation
    • Shape
    • Statistics

    ASJC Scopus subject areas

    • General Computer Science
    • Electrical and Electronic Engineering


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