Improving Specificity in Mammography Using Cross-correlation between Wavelet and Fourier Transform
This work addresses the issue of high false positives in mammography screening for women, but it appears incremental as it builds on existing transform methods without introducing a new paradigm.
The paper tackled the problem of low specificity in mammography for breast cancer detection by applying discrete wavelet and Fourier transforms to extract statistical features, which were then classified using a naive Bayesian classifier, aiming to achieve optimal high specificity.
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.