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  Statistical approach to segmentation of single-channel cerebral MR images

Rajapakse, J., Giedd, J. N., & Rapoport, J. L. (1997). Statistical approach to segmentation of single-channel cerebral MR images. IEEE Transactions on Medical Imaging, 16(2), 176-186. doi:10.1109/42.563663.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-3A78-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-3A79-5
Genre: Journal Article

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 Creators:
Rajapakse, Jagath1, Author              
Giedd, Jay N.1, Author
Rapoport, Judith L.1, Author
Affiliations:
1External Organizations, ou_persistent22              

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Free keywords: Image segmentation; Biological system modeling; Iterative algorithms; Magnetic field measurement; Parameter estimation; Magnetic resonance; Magnetic noise; Biological tissues; Markov random fields; Statistical analysis
 Abstract: A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are modeled by a finite Gaussian mixture. Smoothness and piecewise contiguous nature of the tissue regions are modeled by a three-dimensional (3-D) Markov random field (MRF). A segmentation algorithm, based on the statistical model, approximately finds the maximum a posteriori (MAP) estimation of the segmentation and estimates the model parameters from the image data. The proposed scheme for segmentation is based on the iterative conditional modes (ICM) algorithm in which measurement model parameters are estimated using local information at each site, and the prior model parameters are estimated using the segmentation after each cycle of iterations. Application of the algorithm to a sample of clinical MR brain scans, comparisons of the algorithm with other statistical methods, and a validation study with a phantom are presented. The algorithm constitutes a significant step toward a complete data driven unsupervised approach to segmentation of MR images in the presence of the random noise and intensity inhomogeneities.

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Language(s): eng - English
 Dates: 1997-04
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1109/42.563663
PMID: 9101327
 Degree: -

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Title: IEEE Transactions on Medical Imaging
  Other : IEEE Trans. Med. Imaging
Source Genre: Journal
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Publ. Info: New York, NY : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 16 (2) Sequence Number: - Start / End Page: 176 - 186 Identifier: ISSN: 0278-0062
CoNE: https://pure.mpg.de/cone/journals/resource/954925505280