Contribution of Haar wavelets and MPEG-7 textural features for false positive reduction in a CAD system for the detection of masses in mammograms

The study investigates the significance of wavelet-based and MPEG-7 homogeneous textural features in an attempt to improve the specificity of an in-house CAD system for the detection of masses in screening mammograms. The detection scheme has been presented before and it relies on the concept of morphologic concentric layer (MCL) analysis to identify suspicious locations in a mammogram. The locations were deemed suspicious due to their morphology; especially an increased activity of iso-intensity layers around these locations. On a set of 270 mammographic images, the MCL detection scheme achieved 93% (131/141) mass detection rate with 4.8 FPs/image (1,296/270). In the present study, the textural signature of the detected location is analyzed for possible false positive reduction. For texture analysis, HAAR wavelet and MPEG-7 HTD textural features were extracted. In addition, the contribution of directional neighborhood (DN) features was studied as well. The extracted features were combined with a back-propagation artificial neural network (BPANN) to discriminate true masses from false positives. Using a database of 1,427 suspicious seeds (131 true masses and 1,296 FPs) and a 5-fold cross-validation sampling scheme, the ROC area index of the BPNN using the different sets of features were as follows: Az(HAAR)=0.87±0.01, Az(HTD)=0.91±0.02, Az(DN)=0.84±0.01. Averaging the scores of the three BPANNs resulted in statistically significantly better performance Az(ALL)=0.94±0.01. At 95% sensitivity, the FP rate was reduced by 77.5%. The overall performance of the system after incorporation of textural and directional features was 87.9% sensitivity for malignant masses at 1.1 FPs/image.