Abstract
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 language | English |
|---|---|
| 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 |
| Place of Publication | Berlin, Heidelberg |
| Publisher | Springer Berlin Heidelberg |
| Pages | 138-145 |
| Number of pages | 8 |
| ISBN (Print) | 978-3-642-16239-8 |
| DOIs | |
| Publication status | Published - 2010 |
| Externally published | Yes |
Keywords
- contourlet transform
- ultrasound images
- feature extraction
- thyroid
- feature selection