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.
|Title of host publication||Artificial Intelligence Applications and Innovations|
|Subtitle of host publication||6th IFIP WG 12.5 International Conference, AIAI 2010, Larnaca, Cyprus, October 6-7, 2010, Proceedings|
|Editors||Harris Papadopoulos, Andreas S. Andreou, Max Bramer|
|Publisher||Springer-Verlag Berlin Heidelberg GmbH|
|Number of pages||8|
|ISBN (Print)||9783642162381, 9783642423611|
|Publication status||Published - 2010|
|Name||IFIP Advances in Information and Communication Technology|
- contourlet transform
- ultrasound images
- feature extraction
- feature selection
Katsigiannis, S., Keramidas, E. G., & Maroulis, D. (2010). Contourlet transform for texture representation of ultrasound thyroid images. In H. Papadopoulos, A. S. Andreou, & M. Bramer (Eds.), Artificial Intelligence Applications and Innovations: 6th IFIP WG 12.5 International Conference, AIAI 2010, Larnaca, Cyprus, October 6-7, 2010, Proceedings (1 ed., Vol. 339, pp. 138-145). (IFIP Advances in Information and Communication Technology). Springer-Verlag Berlin Heidelberg GmbH.