Multispectral image classification of MRI data using an empirically-derived clustering algorithm

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Multispectral image analysis of magnetic resonance imaging (MRI) data has been performed using an empirically-derived clustering algorithm. This algorithm groups image pixels into distinct classes which exhibit similar response in the T{sub 2} 1st and 2nd-echo, and T{sub 1} (with ad without gadolinium) MRI images. The grouping is performed in an n-dimensional mathematical space; the n-dimensional volumes bounding each class define each specific tissue type. The classification results are rendered again in real-space by colored-coding each grouped class of pixels (associated with differing tissue types). This classification method is especially well suited for class volumes with complex boundary shapes, and ... continued below

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2 p.

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Horn, K.M.; Osbourn, G.C.; Bouchard, A.M. & Sanders, J.A. August 1, 1998.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 12 times . More information about this article can be viewed below.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Multispectral image analysis of magnetic resonance imaging (MRI) data has been performed using an empirically-derived clustering algorithm. This algorithm groups image pixels into distinct classes which exhibit similar response in the T{sub 2} 1st and 2nd-echo, and T{sub 1} (with ad without gadolinium) MRI images. The grouping is performed in an n-dimensional mathematical space; the n-dimensional volumes bounding each class define each specific tissue type. The classification results are rendered again in real-space by colored-coding each grouped class of pixels (associated with differing tissue types). This classification method is especially well suited for class volumes with complex boundary shapes, and is also expected to robustly detect abnormal tissue classes. The classification process is demonstrated using a three dimensional data set of MRI scans of a human brain tumor.

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2 p.

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OSTI as DE98007228

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  • 1998 IEEE nuclear science symposium and medical imaging conference, Toronto (Canada), 10-12 Nov 1998

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  • Other: DE98007228
  • Report No.: SAND--98-1415C
  • Report No.: CONF-981110--
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 674894
  • Archival Resource Key: ark:/67531/metadc710075

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  • August 1, 1998

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  • Sept. 12, 2015, 6:31 a.m.

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  • May 5, 2016, 8:42 p.m.

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Horn, K.M.; Osbourn, G.C.; Bouchard, A.M. & Sanders, J.A. Multispectral image classification of MRI data using an empirically-derived clustering algorithm, article, August 1, 1998; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc710075/: accessed September 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.