title: Automatic identification of bacterial types using statistical imaging methods.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel. trattner@post.tau.ac.il
authors.forename: Sigal
authors.initials: S
authors.lastname: Trattner
authors.forename: Hayit
authors.initials: H
authors.lastname: Greenspan
authors.forename: Gabi
authors.initials: G
authors.lastname: Tepper
authors.forename: Shimon
authors.initials: S
authors.lastname: Abboud
mesh_terms: D001434:Bacteriophage Typing / Q000379:methods*; D001435:Bacteriophages / Q000145:classification*; D001699:Biometry; D016521:Electrophoresis, Gel, Pulsed-Field; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D013045:Species Specificity; D013204:Staphylococcus Phages / Q000145:classification; D013211:Staphylococcus aureus / Q000145:classification