Contourlet Transform for Texture Representation of Ultrasound Thyroid Images

Stamos Katsigiannis, Eystratios G. Keramidas, Dimitris Maroulis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


Texture representation of ultrasound (US) images is currently considered a major issue in medical image analysis. This paper investigates the texture representation of thyroid tissue via features based on the Contourlet Transform (CT) using different types of filter banks. A variety of statistical texture features based on CT coefficients, have been considered through a selection schema. The Sequential Float Feature Selection (SFFS) algorithm with a k-NN classifier has been applied in order to investigate the most representative set of CT features. For the experimental evaluation a set of normal and nodular ultrasound thyroid textures have been utilized. The maximum classification accuracy was 93%, showing that CT based texture features can be successfully applied for the representation of different types of texture in US thyroid images.
Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations
Subtitle of host publication6th IFIP WG 12.5 International Conference, AIAI 2010, Larnaca, Cyprus, October 6-7, 2010. Proceedings
EditorsHarris Papadopoulos, Andreas S. Andreou, Max Bramer
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Number of pages8
ISBN (Print)978-3-642-16239-8
Publication statusPublished - 2010
Externally publishedYes


  • contourlet transform
  • ultrasound images
  • feature extraction
  • thyroid
  • feature selection


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