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Proceedings Paper

On the Performance of Stochastic Model-Based Image Segmentation
Author(s): Tianhu Lei; Wilfred Sewchand
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Paper Abstract

A new stochastic model-based image segmentation technique for X-ray CT image has been developed and has been extended to the more general nondiffraction CT images which include MRI, SPELT, and certain type of ultrasound images [1,2]. The nondiffraction CT image is modeled by a Finite Normal Mixture. The technique utilizes the information theoretic criterion to detect the number of the region images, uses the Expectation-Maximization algorithm to estimate the parameters of the image, and uses the Bayesian classifier to segment the observed image. How does this technique over/under-estimate the number of the region images? What is the probability of errors in the segmentation of this technique? This paper addresses these two problems and is a continuation of [1,2].

Paper Details

Date Published: 1 November 1989
PDF: 8 pages
Proc. SPIE 1199, Visual Communications and Image Processing IV, (1 November 1989); doi: 10.1117/12.970146
Show Author Affiliations
Tianhu Lei, University of Maryland School of Medicine (United States)
Wilfred Sewchand, University of Maryland School of Medicine (United States)

Published in SPIE Proceedings Vol. 1199:
Visual Communications and Image Processing IV
William A. Pearlman, Editor(s)

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