Mammography image classification using texture features
Date Issued
2012
Author(s)
Jankulovski, Blagojche
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.
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.
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