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
Optimizing optics and imaging for pattern recognition based screening tasks
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Show others and affiliations
2014 (English)In: Proc. 22nd International Conference on Pattern Recognition, IEEE Computer Society, 2014, 3333-3338 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a method for simulating lower quality images starting from higher quality ones, based on acquired image pairs from different optical setups. The method does not require estimates of point (or line) spread functions of the system, but utilizes the relative transfer function derived from images of real specimen of interest in the observed application. Thanks to the use of a larger number of real specimen, excellent stability and robustness of the method is achieved. The intended use is exploring the influence of image quality on features and classification accuracy in pattern recognition based screening tasks. Visual evaluation of the obtained images strongly confirms usefulness of the method. The approach is quantitatively evaluated by observing stability of feature values, proven useful for PAP-smear classification, between synthetic and real images from seven different microscope setups. The evaluation shows that features from the synthetically generated lower resolution images are as similar to features from real images at that resolution, as features from two different images of the same specimen, taken at the same low resolution, are to each other.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014. 3333-3338 p.
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-239584DOI: 10.1109/ICPR.2014.572ISI: 000359818003078ISBN: 978-1-4799-5208-3 (print)OAI: oai:DiVA.org:uu-239584DiVA: diva2:774861
Conference
ICPR 2014, August 24–28, Stockholm, Sweden
Available from: 2014-08-28 Created: 2014-12-29 Last updated: 2017-02-08Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Lindblad, JoakimSladoje, NatasaMalm, PatrikBengtsson, Ewert

Search in DiVA

By author/editor
Lindblad, JoakimSladoje, NatasaMalm, PatrikBengtsson, Ewert
By organisation
Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer Interaction
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 398 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