  Breast cancer is in the most common malignant tumor in women. It accounted
for 30% of new malignant tumor cases. Although the incidence of breast cancer
remains high around the world, the mortality rate has been continuously
reduced. This is mainly due to recent developments in molecular biology
technology and improved level of comprehensive diagnosis and standard
treatment. Early detection by mammography is an integral part of that. The most
common breast abnormalities that may indicate breast cancer are masses and
calcifications. Previous detection approaches usually obtain relatively high
sensitivity but unsatisfactory specificity. We will investigate an approach
that applies the discrete wavelet transform and Fourier transform to parse the
images and extracts statistical features that characterize an image's content,
such as the mean intensity and the skewness of the intensity. A naive Bayesian
classifier uses these features to classify the images. We expect to achieve an
optimal high specificity.
