@inbook{7d7c4c106dc94071acca27934b1561d5,
title = "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.",
keywords = "contourlet transform, ultrasound images, feature extraction, thyroid, feature selection",
author = "Stamos Katsigiannis and Keramidas, {Eystratios G.} and Dimitris Maroulis",
year = "2010",
language = "English",
isbn = "9783642162381",
volume = "339",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer-Verlag Berlin Heidelberg GmbH",
pages = "138--145",
editor = "Harris Papadopoulos and Andreou, {Andreas S.} and Max Bramer",
booktitle = "Artificial Intelligence Applications and Innovations",
address = "Germany",
edition = "1",
}