Adaptive unsupervised contextual Bayesian segmentation: application on images of blood vessel

Anrong Peng; Wojciech Pieczynski

doi:10.1117/12.179267

8 July 1994 Adaptive unsupervised contextual Bayesian segmentation: application on images of blood vessel

Anrong Peng, Wojciech Pieczynski

Proceedings Volume 2299, Mathematical Methods in Medical Imaging III; (1994) https://doi.org/10.1117/12.179267
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

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.

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Anrong Peng and Wojciech Pieczynski "Adaptive unsupervised contextual Bayesian segmentation: application on images of blood vessel", Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); https://doi.org/10.1117/12.179267

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KEYWORDS

Image segmentation

Expectation maximization algorithms

Image processing algorithms and systems

Blood vessels

Radon

Stochastic processes

Image classification

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