Classification of breast tissues using Getis-Ord statistics and support vector machine

Authors:
Geraldo Braz;Anselmo Cardoso Paiva;Aristófanes Corrêa Silva;Alexandre Cêsar Muniz de Oliveira
Affiliations:
Universidade Federal do Maranhão, Bacanga, São Luís, MA, Brazil;Universidade Federal do Maranhão, Bacanga, São Luís, MA, Brazil;Universidade Federal do Maranhão, Bacanga, São Luís, MA, Brazil;Universidade Federal do Maranhão, Bacanga, São Luís, MA, Brazil
Venue:
Intelligent Decision Technologies - Special issue on advances in medical intelligent decision support systems
Year:
2009

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Abstract

Female breast cancer is the major cause of cancer-related deaths in western countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. In this paper, we present a methodology that intends to use Getis Index spatial texture measures in order to distinguish mass and non-mass tissues extracted from mammograms. The computed measures are classified through a One-Class and a Two-Class Support Vector Machine (SVM). The proposed method reaches 99.33% of accuracy using One-Class SVM and 94.21% of accuracy using Two-Class SVM.