Format

Send to

Choose Destination
See comment in PubMed Commons below
IEEE Trans Med Imaging. 2007 Apr;26(4):497-508.

A statistical parts-based model of anatomical variability.

Author information

1
Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada. mtoews@cim.mcgill.ca

Erratum in

  • IEEE Trans Med Imaging. 2007 May;26(5):757.

Abstract

In this paper, we present a statistical parts-based model (PBM) of appearance, applied to the problem of modeling intersubject anatomical variability in magnetic resonance (MR) brain images. In contrast to global image models such as the active appearance model (AAM), the PBM consists of a collection of localized image regions, referred to as parts, whose appearance, geometry and occurrence frequency are quantified statistically. The parts-based approach explicitly addresses the case where one-to-one correspondence does not exist between all subjects in a population due to anatomical differences, as model parts are not required to appear in all subjects. The model is constructed through a fully automatic machine learning algorithm, identifying image patterns that appear with statistical regularity in a large collection of subject images. Parts are represented by generic scale-invariant features, and the model can, therefore, be applied to a wide variety of image domains. Experimentation based on 2-D MR slices shows that a PBM learned from a set of 102 subjects can be robustly fit to 50 new subjects with accuracy comparable to 3 human raters. Additionally, it is shown that unlike global models such as the AAM, PBM fitting is stable in the presence of unexpected, local perturbation.

PMID:
17427737
DOI:
10.1109/TMI.2007.892510
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for IEEE Engineering in Medicine and Biology Society
    Loading ...
    Support Center