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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Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.138-145, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_20〉
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Stamos Katsigiannis, Eystratios G. Keramidas, Dimitris Maroulis. Contourlet Transform for Texture Representation of Ultrasound Thyroid Images. Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.138-145, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_20〉. 〈hal-01060661〉

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