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  Segmentation of MR images with intensity inhomogeneities

Rajapakse, J. C., & Kruggel, F. (1998). Segmentation of MR images with intensity inhomogeneities. Image and Vision Computing, 16(3), 165-180. doi:10.1016/S0262-8856(97)00067-X.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-D742-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-EAB4-8
Genre: Journal Article

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 Creators:
Rajapakse, J. C.1, Author              
Kruggel, F.1, 2, Author              
Affiliations:
1MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634574              
2Department Cognitive Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634563              

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Free keywords: Bias field; Brain imaging; Magnetic resonance images; Image segmentation; Intensity inhomogeneities; Statistical modeling
 Abstract: A statistical model to segment clinical magnetic resonance (MR) images in the presence of noise and intensity inhomogeneities is proposed. Inhomogeneities are considered to be multiplicative low-frequency variations of intensities that are due to the anomalies of the magnetic fields of the scanners. The measurements are modeled as a Gaussian mixture where inhomogeneities present a bias field in the distributions. The piecewise contiguous nature of the segmentation is modeled by a Markov random field (MRF). A greedy algorithm based on the iterative conditional modes (ICM) algorithm is used to find an optimal segmentation while estimating the model parameters. Results with simulated and hand-segmented images are presented to compare performance of the algorithm with other statistical methods. Segmentation results with MR head scans acquired from four different clinical scanners are presented.

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Language(s): eng - English
 Dates: 1998
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: eDoc: 239184
ISI: 000072791200002
Other: P6728
DOI: 10.1016/S0262-8856(97)00067-X
 Degree: -

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Title: Image and Vision Computing
  Other : Image Vis. Comput.
Source Genre: Journal
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Affiliations:
Publ. Info: Guildford, Surrey : Elsevier
Pages: - Volume / Issue: 16 (3) Sequence Number: - Start / End Page: 165 - 180 Identifier: ISSN: 0262-8856
CoNE: /journals/resource/954925498074