Please use this identifier to cite or link to this item:
Title: Mammography image classification using texture features
Authors: Trojachanec, Katarina 
Dimitrovski, Ivica 
Kitanovski, Ivan 
Jankulovski, Blagojche
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.
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers

Files in This Item:
File Description SizeFormat 
9CiiT-27.pdf275.49 kBAdobe PDFView/Open
Show full item record

Page view(s)

checked on Jun 20, 2024


checked on Jun 20, 2024

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.