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Proceedings Paper

Adaptive unsupervised contextual Bayesian segmentation: application on images of blood vessel
Author(s): Anrong Peng; Wojciech Pieczynski
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Paper Abstract

Mixture estimation has been widely applied to unsupervised contextual Bayesian segmentation. We present at first the algorithms which estimate distribution mixtures prior to contextual segmentation, such as estimation-maximization (EM), iterative conditional estimation (ICE), and their adaptive versions valid for nonstationary class fields. Upon removing the stationarity hypothesis, contextual segmentation can give much better results in certain cases. Results obtained attest to the suitability of adaptive versions of EM, ICE valid in the case of nonstationary random class fields. Then we present our experiences on the application of the unsupervised contextual Bayesian segmentation to images of blood vessel.

Paper Details

Date Published: 8 July 1994
PDF: 10 pages
Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); doi: 10.1117/12.179267
Show Author Affiliations
Anrong Peng, Institut National des Telecommunications (France)
Wojciech Pieczynski, Institut National des Telecommunications (France)


Published in SPIE Proceedings Vol. 2299:
Mathematical Methods in Medical Imaging III
Fred L. Bookstein; James S. Duncan; Nicholas Lange; David C. Wilson, Editor(s)

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