• DocumentCode
    765753
  • Title

    Optimization neural networks for the segmentation of magnetic resonance images

  • Author

    Amartur, S.C. ; Piraino, D. ; Takefuji, Y.

  • Author_Institution
    Dept. of Radiol., Case Western Reserve Univ., Cleveland, OH, USA
  • Volume
    11
  • Issue
    2
  • fYear
    1992
  • fDate
    6/1/1992 12:00:00 AM
  • Firstpage
    215
  • Lastpage
    220
  • Abstract
    The application of the Hopfield neural network for the multispectral unsupervised classification of MR images is reported. Winner-take-all neurons were used to obtain a crisp classification map using proton density-weighted and T2-weighted images in the head. The preliminary studies indicate that the number of iterations needed to reach `good´ solutions was nearly constant with the number of clusters chosen for the problem
  • Keywords
    biomedical NMR; neural nets; optimisation; picture processing; Hopfield neural network; MR images; T2-weighted images; clusters; crisp classification map; head; iterations; magnetic resonance images segmentation; multispectral unsupervised classification; optimization neural networks; proton density-weighted images; winner-take-all neurons; Artificial neural networks; Clustering algorithms; Computer architecture; Humans; Image segmentation; Magnetic resonance; Magnetic resonance imaging; Neural networks; Radiology; Visualization;
  • fLanguage
    English
  • Journal_Title
    Medical Imaging, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0278-0062
  • Type

    jour

  • DOI
    10.1109/42.141645
  • Filename
    141645
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=765753