Statistical imaging for modeling and identification of bacterial types

Sigal Trattner, Hayit Greenspan, Gabi Tepper, Shimon Abboud

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

An automatic tool is developed to identify microbiological data types using computer-vision and statistical modeling techniques. In bacteriophage (phage) typing, representative profiles of bacterial types are extracted. Currently, systems rely on the subjective reading of the profiles by a human expert. This process is time-consuming and prone to errors. The statistical methodology presented in this work, provides for an automated, objective and robust analysis of the visual data, along with the ability to cope with increasing data volumes. Validation is performed by a comparison to an expert manual segmentation and labeling of the phage profiles.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMilan Sonka, Ioannis A. Kakadiaris, Jan Kybic
PublisherSpringer Verlag
Pages329-340
Number of pages12
ISBN (Print)3540226753, 9783540226758
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3117
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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