Mammography image classification using texture features

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.