Segmentation of dense 2D bacilli populations

Vallotton, P; Turnbull, L; Whitchurch, C; Mililli, L

Segmentation of dense 2D bacilli populations

Vallotton, P Turnbull, L

Whitchurch, C

Mililli, L

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010, 2010, pp. 82 - 86
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Vallotton, P	en_US
dc.contributor.author	Turnbull, L https://orcid.org/0000-0002-9255-9033	en_US
dc.contributor.author	Whitchurch, C https://orcid.org/0000-0003-2296-3791	en_US
dc.contributor.author	Mililli, L	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010, 2010, pp. 82 - 86	en_US
dc.identifier.isbn	9780769542713	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32940
dc.description.abstract	Bacteria outnumber all other known organisms by far so there is considerable interest in characterizing them in detail and in measuring their diversity, evolution, and dynamics. Here, we present a system capable of identifying rod-like bacteria (bacilli) correctly in high resolution phase contrast images. We use a probabilistic model together with several purpose-designed image features in order to split bacteria at the septum consistently. Our method commits less than 1% error on test images. Our method should also be applicable to study dense 2D systems composed of elongated elements, such as some viruses, molecules, parasites (plasmodium, euglena), diatoms, and crystals. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010	en_US
dc.relation.isbasedon	10.1109/DICTA.2010.23	en_US
dc.title	Segmentation of dense 2D bacilli populations	en_US
dc.type	Conference Proceeding
utslib.for	0605 Microbiology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - ithree - Institute of Infection, Immunity and Innovation
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Bacteria outnumber all other known organisms by far so there is considerable interest in characterizing them in detail and in measuring their diversity, evolution, and dynamics. Here, we present a system capable of identifying rod-like bacteria (bacilli) correctly in high resolution phase contrast images. We use a probabilistic model together with several purpose-designed image features in order to split bacteria at the septum consistently. Our method commits less than 1% error on test images. Our method should also be applicable to study dense 2D systems composed of elongated elements, such as some viruses, molecules, parasites (plasmodium, euglena), diatoms, and crystals. © 2010 IEEE.

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http://hdl.handle.net/10453/32940