Automatic identification of bacterial types using statistical imaging methods

Sigal Trattner, Hayit Greenspan*, Gabi Tepper, Shimon Abboud

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


The objective of the current study is to develop an automatic tool to identify microbiological data types using computer-vision and statistical modeling techniques. Bacteriophage (phage) typing methods are used to identify and extract representative profiles of bacterial types out of species such as the Staphylococcus aureus. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors, especially as technology is enabling the increase in the number of phages used for typing. The statistical methodology presented in this work, provides for an automated, objective and robust analysis of visual data, along with the ability to cope with increasing data volumes.

Original languageEnglish
Pages (from-to)807-820
Number of pages14
JournalIEEE Transactions on Medical Imaging
Issue number7
StatePublished - Jul 2004


  • Bacteria image analysis
  • Phage typing
  • Spot finding
  • Statistical modeling
  • Visual-array data


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