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Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm (2001)
| Venue: | IEEE TRANSACTIONS ON MEDICAL. IMAGING |
| Citations: | 639 - 15 self |
BibTeX
@ARTICLE{Zhang01segmentationof,
author = {Yongyue Zhang and Michael Brady and Stephen Smith},
title = {Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm},
journal = {IEEE TRANSACTIONS ON MEDICAL. IMAGING},
year = {2001}
}
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Abstract
The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation—no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.
Keyphrases
brain mr hmrf model expectationmaximization algorithm hidden markov random field model hmrf-em framework em algorithm spatial information fm model mutual influence piecewise constant nature fm model-based method low level mr image segmentation ideal brain mr image bias field distortion unreliable result degenerate version hidden markov random field fm model-based approach robust segmentation intrinsic limitation used model brain mr image segmentation bias field correction algorithm histogram-based model general prior stochastic process novel hidden markov random field state sequence cannot hmrf model derives index term bias field correction statistical segmentation brain magnetic resonance simple mathematical form mrf modeling well-defined image partial volume effect finite mixture
