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Proceedings Paper

Corpus callosum analysis using MDL-based sequential models of shape and appearance
Author(s): Mikkel B. Stegmann; Rhodri H. Davies; Charlotte Ryberg
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Paper Abstract

This paper describes a method for automatically analysing and segmenting the corpus callosum from magnetic resonance images of the brain based on the widely used Active Appearance Models (AAMs) by Cootes et al. Extensions of the original method, which are designed to improve this specific case are proposed, but all remain applicable to other domain problems. The well-known multi-resolution AAM optimisation is extended to include sequential relaxations on texture resolution, model coverage and model parameter constraints. Fully unsupervised analysis is obtained by exploiting model parameter convergence limits and a maximum likelihood estimate of shape and pose. Further, the important problem of modelling object neighbourhood is addressed. Finally, we describe how correspondence across images is achieved by selecting the minimum description length (MDL) landmarks from a set of training boundaries using the recently proposed method of Davies et al. This MDL-approach ensures a unique parameterisation of corpus callosum contour variation, which is crucial for neurological studies that compare reference areas such as rostrum, splenium, et cetera. We present quantitative and qualitative results that show that the method produces accurate, robust and rapid segmentations in a cross sectional study of 17 subjects, establishing its feasibility as a fully automated clinical tool for analysis and segmentation.

Paper Details

Date Published: 12 May 2004
PDF: 8 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535656
Show Author Affiliations
Mikkel B. Stegmann, Technical Univ. of Denmark (Denmark)
Danish Research Ctr. for Magnetic Resonance (Denmark)
Rhodri H. Davies, Univ. of Melbourne (Australia)
Charlotte Ryberg, Danish Research Ctr. for Magnetic Resonance (Denmark)


Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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