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Cited by 6,982

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

Open access
Zhang, Y., Brady, M., Smith, S.·2001·IEEE Transactions on Medical Imaging
DOI 10.1109/42.906424
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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.

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Show all 3 references

Research works printed but not linked: 18

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  2. 10.1007/bfb0056231 · DOI
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  7. Brummer, 1992, Optimized intensity thresholds for volumetric analysis of magnetic resonance imaging data, Proc. SPIE, 1808, 299, 10.1117/12.131086 · doi:10.1117/12.131086
  8. 10.1016/0895-6111(90)90057-I · doi:10.1016/0895-6111(90)90057-i
  9. 10.1109/42.511747 · DOI · Paper page
  10. 10.1109/TPAMI.1984.4767596 · DOI · Paper page
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  12. Li, 1995, Markov Random Field Modeling in Computer Vision, 10.1007/978-4-431-66933-3 · DOI · Paper page
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  14. 10.1111/j.2517-6161.1974.tb00999.x · DOI · Paper page
  15. 10.1109/83.199925 · DOI
  16. 10.1016/0031-3203(89)90076-9 · DOI
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  18. 10.1109/TSMC.1979.4310076 · DOI · Paper page

Cited by

Metadata

Authors
Zhang, Y., Brady, M., & Smith, S.
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2001
Volume
20
Issue
1
Pages
45-57
ISSN
0278-0062 (ISSN-L)
DOI
10.1109/42.906424
OpenAlex
W2136573752
Cited by
6,982
References
3
FWCI
52.95
Type
journal article
Language
en
Open access
green
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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