The use of contextual information for computer aided detection of masses in mammograms

Abstract

In breast cancer screening, radiologists not only look at local properties of suspicious regions in the mammogram but take also into account more general contextual information. In this study we investigated the use of similar information for computer aided detection of malignant masses. We developed a new set of features that combine information from the candidate mass region and the whole image or mammogram. The developed context features were constructed to give information about suspiciousness of a region relative to other areas in the mammogram, the location in the image, the location in relation to dense tissue and the overall amount of dense tissue in the mammogram. We used a step-wise floating feature selection algorithm to select subsets from the set of available features. Feature selection was performed two times, once using the complete feature set (37 context and 40 local features) and once using only the local features. It was found that in the subsets selected from the complete feature set 30-60% were context features. At most one local feature present in the subset containing context features was not present in the subset without context features. We validated the performance of the selected subsets on a separate data set using cross validation and bootstrapping. For each subset size we compared the performance obtained using the features selected from the complete feature set to the performance obtained using the features selected from the local feature set. We found that subsets containing context features performed significantly better than feature sets containing no context features.