Unsupervised image segementation by stochastic reconstruction
- 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.
Author: | Volker H. Metzler, Ralf Vandenhouten, Jörg Krone, Reinhard Grebe |
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URN: | urn:nbn:de:kobv:526-opus4-9329 |
DOI (Version of Record): | https://doi.org/10.1117/12.310935 |
ISSN: | 1996-756X |
Parent Title (English): | Proceedings of SPIE |
Document Type: | Conference Proceeding |
Language: | English |
Year of Publication: | 1998 |
Publishing Institution: | Technische Hochschule Wildau |
Release Date: | 2017/04/20 |
Volume: | 3338 |
Source: | Volker H. Metzler ; Ralf Vandenhouten ; Joerg Krone and Reinhard Grebe, "Unsupervised image segementation by stochastic reconstruction", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, 575 (June 24, 1998). |
Copyright: | Copyright 1998 Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited. http://dx.doi.org/10.1117/12.310935 |
Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
Licence (German): | Das Dokument ist urheberrechtlich geschützt |