A new stochastic model-based image segmentation technique for MR image

A new framework for stochastic MR image modeling is presented based on local randomization, and a new model-based MR image analysis technique is developed in terms of stochastic regularization. By discussing the statistical properties of both pixel image and context image, an inhomogeneous hidden Ma...

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Bibliographic Details
Main Authors: Yue Wang, Tianhu Lei
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Context modeling
Hidden Markov models
Image edge detection
Image segmentation
Maximum likelihood detection
Maximum likelihood estimation
Parameter estimation
Pixel
Stochastic processes
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Summary:A new framework for stochastic MR image modeling is presented based on local randomization, and a new model-based MR image analysis technique is developed in terms of stochastic regularization. By discussing the statistical properties of both pixel image and context image, an inhomogeneous hidden Markov random field model is proposed and justified for MR image. The solution of the new problem formulation is implemented with an efficient multistage procedure. The number of image regions is detected by a new information criterion. The parameters in the model are estimated from block-wise classification-maximization and Bayesian maximum likelihood algorithms. The image segmentation is performed via a maximum a posteriori probability classifier. The experimental results with simulated and real MR images are provided to demonstrate the promise and effectiveness of the proposed technique.< >
DOI:10.1109/ICIP.1994.413556