Contourlet Transform for Texture Representation of Ultrasound Thyroid Images

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.
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Stamos Katsigiannis, Eystratios G. Keramidas, Dimitris Maroulis. Contourlet Transform for Texture Representation of Ultrasound Thyroid Images. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. pp.138-145, ⟨10.1007/978-3-642-16239-8_20⟩. ⟨hal-01060661⟩

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