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Segmentation of Magnetic resonance brain images using analog constraint satisfaction neural networks

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Information Processing in Medical Imaging (IPMI 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 687))

Abstract

The Grey-White Decision Network (GWDN) is presented as an analog constraint satisfaction neural network that segments magnetic resonance brain images. Constraints on signal intensity, neighborhood interactions and edge influences are combined to assign labels of grey matter, white matter or “other” to each pixel. An improved version of this novel segmentation network that is provably stable is described. Results of the network are presented along with a comparison of these results to a collection of human segmentations. The network is discussed in relation to other methods for segmentation and the network's extendibility is described.

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Authors and Affiliations

  1. Center for Morphometric Analysis, Neuroscience Center, Massachusetts General Hospital-East, Building 149, 13th St., 02129, Charlestown, MA

    Andrew J. Worth & David N. Kennedy

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  1. Andrew J. Worth
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  2. David N. Kennedy
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Editor information

Harrison H. Barrett A. F. Gmitro

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© 1993 Springer-Verlag Berlin Heidelberg

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Worth, A.J., Kennedy, D.N. (1993). Segmentation of Magnetic resonance brain images using analog constraint satisfaction neural networks. In: Barrett, H.H., Gmitro, A.F. (eds) Information Processing in Medical Imaging. IPMI 1993. Lecture Notes in Computer Science, vol 687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0013791

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  • DOI: https://doi.org/10.1007/BFb0013791

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56800-1

  • Online ISBN: 978-3-540-47742-6

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