Normal mammogram classification based on regional analysis

The majority of screening mammograms are normal. It will be beneficial if a detection system is designed to help radiologists readily identify normal regions of mammograms. In this paper, we will present a binary tree classifier based on the use of global features extracted from different levels of a 2-D Quincunx wavelet decomposition of normal and abnormal regional images. This classifier is then used to classify whether an entire whole-field mammogram is normal. This approach is fundamentally different from other approaches that identify a particular abnormality in that is independent of the particular type of abnormality.