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

Issue title: Advances in medical intelligent decision support systems

Guest editors: Vassilis Kodogiannisx, Ilias Petrouniasy and John Lygourasz

Article type: Research Article

Authors: Junior, Geraldo Braz^{; *} | Paiva, Anselmo Cardoso | Silva, Aristófanes Corrêa | de Oliveira, Alexandre Cêsar Muniz

Affiliations: Universidade Federal do Maranhão - UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil | [x] University of Westminster, United Kingdom | [y] The University of Manchester, United Kingdom | [z] Democritus University of Thrace, Greece

Correspondence: [*] Corresponding author. E-mail: [email protected]

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.

Keywords: Mammogram, CADx, spatial texture analysis, SVM, One-Class SVM

DOI: 10.3233/IDT-2009-0055

Journal: Intelligent Decision Technologies, vol. 3, no. 4, pp. 197-205, 2009