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Proceedings Paper

Performance evaluation of unsupervised stochastic model-based image segmentation technique
Author(s): Tianhu Lei; Wilfred Sewchand
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Paper Abstract

This paper provides a new approach for performance evaluation of unsupervised stochastic model-based image segmentation techniques. Performance evaluation is conducted at three (3) aspects: (1) ability in detection of the number of image regions, (2) accuracy in estimation of the model parameters, and (3) error in classification of pixels into image regions. For detection performance, probabilities of over- detection and under-detection of the number of image regions are defined, and the corresponding formulae in terms of model parameters and image quality are derived. For estimation performance, this paper shows that both Classification-Maximization (CM) and Expectation-Maximization (EM) algorithms produce the asymptotically unbiased ML estimates of model parameters in the case of no-overlap. Cramer-Rao bounds of variances of these estimates are derived. For classification performance, misclassification probability, based on parameter estimate and classified data, is derived to evaluate segmentation errors. The results by applying this performance evaluation method to the simulated images demonstrate that for the images with the moderate quality, the detection procedure is robust, the parameter estimates are accurate, and the segmentation errors are small.

Paper Details

Date Published: 21 April 1995
PDF: 12 pages
Proc. SPIE 2501, Visual Communications and Image Processing '95, (21 April 1995); doi: 10.1117/12.206695
Show Author Affiliations
Tianhu Lei, Univ. of Maryland School of Medicine (United States)
Wilfred Sewchand, Univ. of Maryland School of Medicine (United States)


Published in SPIE Proceedings Vol. 2501:
Visual Communications and Image Processing '95
Lance T. Wu, Editor(s)

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