Extracting and Selecting Meaningful Features from Mammogram Digital Image Using Factor Analysis Technique

Abstract

Breast cancer is the most common cancer in women around the world. Various countries including the United Arab Emirates (UAE) offer asymptomatic screening for the disease. The interpretation of mammograms is a very challenges task and is subject  to human error. However the Mammography is considered to be a significant method for detection of breast tumors. Hence, finding an accurate and effective diagnostic method is very important to increase survival rate and reduce mortality of the women. Not all the features in the image give the characteristics and information of the image however, only some extracted features that can express enough information about the image. In this research statistical features selection methods have been developed with association with statistical techniques. 141 ROIs extracted from Digital Database for Screening Mammography (DDSM) has been used to contact this research. Our experiment was classified into two stages in order to reduce the image features which extracted from the ROIs. In the first stage we applied statistical techniques to reduce feature with high accuracy rate, in the second stage we applied graph based method and bayesian inference. Our method was able to achieve high accuracy compared to the original selected features.