Computer-Assisted Diagnosis System for Abnormalities Classification in Digital Mammography Based on Multi-Threshold Modified Local Ternary Pattern (MtMLTP)

Abstract

The aim of this paper is to develop an efficient breast cancer Computer Aided Diagnosis (CAD) system allowing the analysis of different breast tissues in mammograms and performing textural classification (normal, mass or microcalcification). Although several feature extraction algorithms for breast tissues analysis have been used, the findings concerning tissue characterization show no consensus in the literature. Specifically, the challenge may be great for mass and microcalcification detection on dense breasts. The proposed system is based on the development of a new feature extraction approach, the latter is called Multi-threshold Modified Local Ternary Pattern (MtMLTP), it allows the discrimination between various tissues in mammographic images allowing significant improvements in breast cancer diagnosis. In this paper, we have used 1000 ROIs obtained from Digital Database for Screening Mammography (DDSM) database and 100 ROIs from a local Tunisian database named Tunisian Digital Database for Screening Mammography (TDDSM). The Artificial Neural Network (ANN) shows good performance in the classification of abnormalities since the Area Under the Curve (AUC) of the proposed system has been found to be 0.97 for the DDSM database and 0.99 for the TDDSM Database.