Abstract
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 language | English (US) |
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Pages (from-to) | 1670-1679 |
Number of pages | 10 |
Journal | Proceedings of the IEEE |
Volume | 91 |
Issue number | 10 |
DOIs | |
State | Published - Oct 2003 |
Keywords
- Discrimination
- Medial
- Medical image
- Multiscale
- Object
- Registration
- Segmentation
- Shape
- Statistics
ASJC Scopus subject areas
- Electrical and Electronic Engineering