Detection of Breast Masses in Mammogram Images Using Growing Neural Gas Algorithm and Ripley's K Function

Authors:
Leonardo Oliveira Martins;Aristófanes Corrêa Silva;Anselmo Cardoso De Paiva;Marcelo Gattass
Affiliations:
Department of Electrical Engineering, Federal University of Maranhão--UFMA, São Luís, Brazil 65085-580;Department of Electrical Engineering, Federal University of Maranhão--UFMA, São Luís, Brazil 65085-580;Department of Computer Science, Federal University of Maranhão--UFMA, São Luís, Brazil 65085-580;Departament of Informatics, Pontifical Catholic University of Rio de Janeiro, Technical Scientific Center, Rio de Janeiro, Brazil 22453-900
Venue:
Journal of Signal Processing Systems
Year:
2009

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Abstract

Breast cancer is a serious public health problem in several countries. Computer-aided detection/diagnosis systems (CAD/CADx) have been used with relative success in aid of health care professionals. The goal of such systems is not to replace the professionals, but to join forces in order to detect the different types of cancer at an early stage. The main contribution of this work is the presentation of a methodology for detecting masses in digitized mammograms using the growing neural gas algorithm for image segmentation and Ripley's K function to describe the texture of segmented structures. The classification of these structures is accomplished through support vector machines which separate them in two groups, using shape and texture measures: masses and non-masses. The methodology obtained 89.30% of accuracy and a rate of 0.93 false positives per image.