hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • 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
Prototype-Based Contour Detection Applied to Segmentation of Phytoplankton Images
Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Kaunas University of Technology, Kaunas, Lithuania.
Kaunas University of Technology, Kaunas, Lithuania.
Show others and affiliations
2013 (English)In: AWERProcedia Information Technology and Computer Science: 3rd World Conference on Information Technology (WCIT-2012) / [ed] Hafize Keser and Meltem Hakiz, 2013, 1285-1292 p.Conference paper, (Refereed)
Abstract [en]

Novel prototype-based framework for image segmentation is introduced and successfully applied for cell segmentation in microscopy imagery. This study is concerned with precise contour detection for objects representing the Prorocentrum minimum species in phytoplankton images. The framework requires a single object with the ground truth contour as a prototype to perform detection of the contour for the remaining objects. The level set method is chosen as a segmentation algorithm and its parameters are tuned by differential evolution. The fitness function is based on the distance between pixels near contour in the prototype image and pixels near detected contour in the target image. Pixels “of interest correspond to several concentric bands of various width in outer and inner areas, relative to the contour. Usefulness of the introduced approach was demonstrated by comparing it to the basic level set and advanced Weka segmentation techniques. Solving the parameter selection problem of the level set algorithm considerably improved segmentation accuracy.

Place, publisher, year, edition, pages
2013. 1285-1292 p.
Series
AWERProcedia, ISSN 1247-5105 ; 3
Keyword [en]
Contour detection, level set, trainable segmentation, differential evolution, Quadratic-Chi distance
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-23643OAI: oai:DiVA.org:hh-23643DiVA: diva2:650739
Conference
3rd World Conference on Information Technology (WCIT-2012), 14-16 November 2012, University of Barcelon, Barcelona, Spain
Available from: 2013-09-23 Created: 2013-09-23 Last updated: 2014-11-10Bibliographically approved

Open Access in DiVA

fulltext(992 kB)463 downloads
File information
File name FULLTEXT01.pdfFile size 992 kBChecksum SHA-512
cc16423d52e9a5a637999335e118b337d358075895cd14bbb46c84d6c872c2fcac3808e5e5c8eaec885a8479af2c305e7a95a77827cce925afe64f25f36591c4
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Verikas, Antanas
By organisation
CAISR - Center for Applied Intelligent Systems Research
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 463 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 172 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • 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