Classification of breast tissues using Getis-Ord statistics and support vector machine
Geraldo Bráz, Anselmo Cardoso de Paiva, Aristófanes Corrêa Silva, Alexandre Cêsar Muniz de Oliveira · Intelligent Decision Technologies · 2009
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 or der 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.