Improving Specificity in Mammography Using Cross-correlation between Wavelet and Fourier Transform

TL;DR

This paper proposes a novel method combining wavelet and Fourier transforms to improve the specificity of mammography image classification, aiming to better distinguish benign from malignant breast abnormalities.

Contribution

It introduces a cross-correlation approach using wavelet and Fourier transforms for feature extraction in mammography, enhancing specificity over traditional methods.

Findings

Achieved higher specificity in classifying mammogram images.

Utilized statistical features like mean intensity and skewness for improved accuracy.

Applied naive Bayesian classifier for effective image categorization.

Abstract

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.