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. © 2010 IFIP.
CITATION STYLE
Katsigiannis, S., Keramidas, E. G., & Maroulis, D. (2010). Contourlet transform for texture representation of ultrasound thyroid images. In IFIP Advances in Information and Communication Technology (Vol. 339 AICT, pp. 138–145). Springer New York LLC. https://doi.org/10.1007/978-3-642-16239-8_20
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