Optimization neural networks for the segmentation of magnetic resonance images

S. C. Amartur, D. Piraino, Yoshiyasu Takefuji

Research output: Contribution to journalArticle

119 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)215-220
Number of pages6
JournalIEEE Transactions on Medical Imaging
Volume11
Issue number2
DOIs
Publication statusPublished - 1992 Jun
Externally publishedYes

Fingerprint

Magnetic resonance
Magnetic Resonance Spectroscopy
Neural networks
Hopfield neural networks
Neurons
Protons
Head

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Optimization neural networks for the segmentation of magnetic resonance images. / Amartur, S. C.; Piraino, D.; Takefuji, Yoshiyasu.

In: IEEE Transactions on Medical Imaging, Vol. 11, No. 2, 06.1992, p. 215-220.

Research output: Contribution to journalArticle

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