Statistical imaging for modeling and identification of bacterial types

Sigal Trattner; Hayit Greenspan; Gabi Tepper; Shimon Abboud

doi:10.1007/978-3-540-27816-0_28

Statistical imaging for modeling and identification of bacterial types

Sigal Trattner
, Hayit Greenspan
, Gabi Tepper
, Shimon Abboud

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

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 language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Milan Sonka, Ioannis A. Kakadiaris, Jan Kybic
Publisher	Springer Verlag
Pages	329-340
Number of pages	12
ISBN (Print)	3540226753, 9783540226758
DOIs	https://doi.org/10.1007/978-3-540-27816-0_28
State	Published - 2004
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3117
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-27816-0_28

Cite this

Trattner, S., Greenspan, H., Tepper, G., & Abboud, S. (2004). Statistical imaging for modeling and identification of bacterial types. In M. Sonka, I. A. Kakadiaris, & J. Kybic (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 329-340). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3117). Springer Verlag. https://doi.org/10.1007/978-3-540-27816-0_28

Trattner, Sigal ; Greenspan, Hayit ; Tepper, Gabi et al. / Statistical imaging for modeling and identification of bacterial types. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Milan Sonka ; Ioannis A. Kakadiaris ; Jan Kybic. Springer Verlag, 2004. pp. 329-340 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Trattner, S, Greenspan, H, Tepper, G & Abboud, S 2004, Statistical imaging for modeling and identification of bacterial types. in M Sonka, IA Kakadiaris & J Kybic (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3117, Springer Verlag, pp. 329-340. https://doi.org/10.1007/978-3-540-27816-0_28

Statistical imaging for modeling and identification of bacterial types. / Trattner, Sigal; Greenspan, Hayit; Tepper, Gabi et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Milan Sonka; Ioannis A. Kakadiaris; Jan Kybic. Springer Verlag, 2004. p. 329-340 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3117).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Abboud, Shimon

PY - 2004

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N2 - 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.

AB - 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.

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DO - 10.1007/978-3-540-27816-0_28

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SN - 3540226753

SN - 9783540226758

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 340

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Trattner S, Greenspan H, Tepper G, Abboud S. Statistical imaging for modeling and identification of bacterial types. In Sonka M, Kakadiaris IA, Kybic J, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 329-340. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-27816-0_28

Statistical imaging for modeling and identification of bacterial types

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