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Categorizing laryngeal images for decision support
Department of Applied Electronics, Kaunas University of Technology, Lithuania.
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
Department of Applied Electronics, Kaunas University of Technology, Lithuania.
2007 (English)In: Advanced Concepts for Intelligent Vision Systems: 9th International Conference, ACIVS 2007, Delft, The Netherlands, August 28-31, 2007 ; proceedings / [ed] Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Berlin: Springer Berlin/Heidelberg, 2007, p. 521-530Chapter in book (Refereed)
Abstract [en]

This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2007. p. 521-530
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; Volume 4678/2007
Keywords [en]
Automated analysis, Laryngeal diseases, Vocal fold images
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hh:diva-2065DOI: 10.1007/978-3-540-74607-2_47Local ID: 2082/2460ISBN: 978-3-540-74606-5 OAI: oai:DiVA.org:hh-2065DiVA, id: diva2:239283
Available from: 2008-10-20 Created: 2008-10-20 Last updated: 2018-01-13Bibliographically approved

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Verikas, Antanas

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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