Wavelet Packet Texture Descriptors Based Four-class BIRADS Breast Tissue Density Classification

The present work proposes a computer aided diagnostic (CAD) system for four-class BIRADS breast tissue density classification. The study has been carried out on a total of 480 mammograms taken from the DDSM database. The ROI has been extracted manually from each image. Each ROI has been decomposed up to 2nd level of decomposition using wavelet packet transform, resulting in 16 sub-band images for each ROI image. From each sub-band image three multi-resolution texture descriptors (i.e., mean, standard deviation and energy descriptors) are computed, resulting in texture feature vector of length 48 (16 X 3) for each ROI. The results obtained from the present work, indicate that the standard deviation and energy descriptors computed from sub-band images obtained by using wavelet packet transform with haar wavelet filter yield the highest overall classification accuracy of 73.7% for four-class breast tissue density classification. Thus it can be concluded that standard deviation and energy multi-resolution texture descriptors computed from sub-band images yielded by wavelet packet transform using haar wavelet filter contain significant information for differential diagnosis between four BIRADS breast tissue density classes.