Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/17851
Title: Mammography image classification using texture features
Authors: Kitanovski, Ivan 
Jankulovski, Blagojce
Trojacanec, Katarina
Dimitrovski, Ivica 
Issue Date: 2012
Publisher: Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia
Conference: CIIT 2012
Abstract: Mammography image classification is a very important research field due to its implementation domain. The aim of this paper is propose techniques for automation of the mammography image classification process. This requires the images to be described using feature extraction algorithms and then classified using machine learning algorithms. In that context, the goal is to find which combination of feature extraction algorithm and classification algorithm yield the best results for mammography image classification. The following feature extraction methods were used LBP, GLDM, GLRLM, Haralick, Gabor filters and a combined descriptor. The images were classified using several machine learning algorithms i.e. support vector machines, random forests and k-nearest neighbour classifier. The best results were obtained when the images were described using GLDM together with the support vector machines as a classification technique.
URI: http://hdl.handle.net/20.500.12188/17851
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers

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