Quantification and segmentation of brain tissues from MR images: A probabilistic neural network approach

Yue Wang, Tülay Adali, Sun Yuan Kung, Zsolt Szabo

Research output: Contribution to journalArticle


This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches.

Original languageEnglish (US)
Pages (from-to)1165-1181
Number of pages17
JournalIEEE Transactions on Image Processing
Issue number8
StatePublished - Dec 1 1998


  • Finite mixture models
  • Image segmentation
  • Information theoretic criteria
  • Model estimation
  • Probabilistic neural networks
  • Relaxation algorithm

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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