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Proceedings Paper

Unsupervised image segementation by stochastic reconstruction
Author(s): Volker H. Metzler; Ralf Vandenhouten; Joerg Krone; Reinhard Grebe
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Paper Abstract

To segment complex and versatile image data from different modalities it is almost impossible to achieve satisfying results without the consideration of contextual information. In this approach, image segmentation is regarded as a high- dimensional optimization task, that can be solved by stochastical methods like evolutionary algorithms (EA). Initially, the iterative algorithm is provided with a set of good-quality sample segmentations. An efficient EA-based learning strategy generates a segmentation for a given target image from the provided samples. This two-level process consists of a global image-based optimization whose convergence is enhanced by locally operating pixel-based Boltzmann processes which restrict the search space to reasonable subsets. The stochastic reconstruction extracts the relevant information from the samples in order to adapt it onto the current segmentation problem, which results in a consistent labeling for the target image. The algorithm works unsupervised, because the range of possible labels and their contextual interpretation is provided implicitly by the sample segmentations. To prove the usefulness of the method experimental results based on both, reproducible phantom images and physiological NMR scans are presented. Moreover, an analysis of the basic segmentation and convergence properties is provided.

Paper Details

Date Published: 24 June 1998
PDF: 12 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310935
Show Author Affiliations
Volker H. Metzler, Aachen Univ. of Technology (Germany)
Ralf Vandenhouten, Aachen Univ. of Technology (Germany)
Joerg Krone, Iserlohn Polytechnical School (Germany)
Reinhard Grebe, Technical Univ. of Compiegne (France)


Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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