uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Robust cell image segmentation methods.
Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2004 (English)In: Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, ISSN 1054-6618, Vol. 14, no 2, 157-167 p.Article in journal (Refereed) Published
Abstract [en]

Biomedical cell image analysis is one of the main application fields of computerized image analysis. This paper outlines the field and the different analysis steps related to it. Relative advantages of different approaches to the crucial step of image segmentation are discussed. Cell image segmentation can be seen as a modeling problem where different approaches are more or less explicitly based on cell models. For example, thresholding methods can be seen as being based on a model stating that cells have an intensity that is different from the surroundings. More robust segmentation can be obtained if a combination of features, such as intensity, edge gradients, and cellular shape, is used. The seeded watershed transform is proposed as the most useful tool for incorporating such features into the cell model. These concepts are illustrated by three real-world problems.

Place, publisher, year, edition, pages
2004. Vol. 14, no 2, 157-167 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-67593OAI: oai:DiVA.org:uu-67593DiVA: diva2:95504
Available from: 2005-05-23 Created: 2005-05-23 Last updated: 2017-02-08

Open Access in DiVA

No full text

Other links

http://www.maik.ru/cgi-bin/search.pl?type=abstract&name=patrec&number=2&year=4&page=157

Authority records BETA

Bengtsson, EwertWählby, Carolina

Search in DiVA

By author/editor
Bengtsson, EwertWählby, Carolina
By organisation
Centre for Image AnalysisComputerized Image Analysis
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 559 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf