Automatic cell segmentation in microscopic color images using ellipse fitting and watershed

Park, M; Jin, JS; Peng, Y; Summons, P; Yu, D; Cui, Y; Luo, S; Wang, F; Santos, L; Xu, M

Automatic cell segmentation in microscopic color images using ellipse fitting and watershed

Park, M Jin, JS Peng, Y Summons, P Yu, D Cui, Y Luo, S Wang, F Santos, L Xu, M

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Publication Type:: Conference Proceeding
Citation:: 2010 IEEE/ICME International Conference on Complex Medical Engineering, CME2010, 2010, pp. 69 - 74
Issue Date:: 2010-10-18

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Field	Value	Language
dc.contributor.author	Park, M	en_US
dc.contributor.author	Jin, JS	en_US
dc.contributor.author	Peng, Y	en_US
dc.contributor.author	Summons, P	en_US
dc.contributor.author	Yu, D	en_US
dc.contributor.author	Cui, Y	en_US
dc.contributor.author	Luo, S	en_US
dc.contributor.author	Wang, F	en_US
dc.contributor.author	Santos, L	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.date.issued	2010-10-18	en_US
dc.identifier.citation	2010 IEEE/ICME International Conference on Complex Medical Engineering, CME2010, 2010, pp. 69 - 74	en_US
dc.identifier.isbn	9781424468430	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16540
dc.description.abstract	This paper presents an efficient and innovative method for the automated counting of cells in a microscopic image. The performance of watershed-based algorithms for the segmentation of clustered cells has been well demonstrated. The strength of our algorithm lies in the fact that it incorporates knowledge of color in the image. Our method uses the watershed transform with iterative shape alignment and is shown to be more accurate in retaining cell shape. We report a sensitivity of 97% and specificity of 96% when all color bands are used. Our methods could be of value to computer-based systems designed to objectively interpret microscopic images, since they provide a means for accurate cell segmentation. © 2010 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP0669645
dc.relation.ispartof	2010 IEEE/ICME International Conference on Complex Medical Engineering, CME2010	en_US
dc.relation.isbasedon	10.1109/ICCME.2010.5558869	en_US
dc.title	Automatic cell segmentation in microscopic color images using ellipse fitting and watershed	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Gold Coast	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 Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents an efficient and innovative method for the automated counting of cells in a microscopic image. The performance of watershed-based algorithms for the segmentation of clustered cells has been well demonstrated. The strength of our algorithm lies in the fact that it incorporates knowledge of color in the image. Our method uses the watershed transform with iterative shape alignment and is shown to be more accurate in retaining cell shape. We report a sensitivity of 97% and specificity of 96% when all color bands are used. Our methods could be of value to computer-based systems designed to objectively interpret microscopic images, since they provide a means for accurate cell segmentation. © 2010 IEEE.

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http://hdl.handle.net/10453/16540