Classification of fluorescence in situ hybridization images using belief networks

Roy Malka, Boaz Lerner

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning.

Original languageEnglish
Pages (from-to)1777-1785
Number of pages9
JournalPattern Recognition Letters
Issue number16
StatePublished - 1 Dec 2004


  • Belief networks
  • Fluorescence in situ hybridization (FISH)
  • Image classification
  • K2 algorithm
  • Naive Bayesian classifier

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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