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Proceedings Paper

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

Since both detection and classification in an unsupervised stochastic model-based segmentation technique require the results from estimation, model parameter estimation becomes a core part of these types of techniques. Based on FNM model, ML likelihood equations and their solutions have been derived. After investigating the Bayesian probability in these solutions and comparing it with probability membership of EM algorithm, this paper proves that both CM and EM algorithms which are used in this segmentation technique do produce asymptotically unbiased ML estimates of FNM parameters, through the iterative process, when the stopping criteria are satisfied. The evolution of probability membership in the iterative process finally leads to the pixel labeling. The corresponding Cramer-Rao bounds of the variances of these estimates are also derived. The results by applying this performance analysis method to the simulated images with signal-to-noise (SNR) as a parameter, are obtained and reported in this paper. These results show that for the images with the moderate quality, the parameter estimates are quite accurate. When SNR >= 14.5 db, all estimates are within the one- standard deviation interval.

Paper Details

Date Published: 12 May 1995
PDF: 11 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208691
Show Author Affiliations
Tianhu Lei, Univ. of Maryland/Baltimore School of Medicine (United States)
Wilfred Sewchand, Univ. of Maryland/Baltimore School of Medicine (United States)


Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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