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.

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author:Volker H. Metzler, Ralf Vandenhouten, Jörg Krone, Reinhard Grebe
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
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.