One-Class Classification of Mammograms Using Trace Transform Functionals

Mammography is one of the first diagnostic tests to prescreen breast cancer. Early detection of breast cancer has been known to improve recovery rates to a great extent. In most medical centers, experienced radiologists are given the responsibility of analyzing mammograms. But, there is always a possibility of human error. Errors can frequently occur as a result of fatigue of the observer, resulting in interobserver and intraobserver variations. The sensitivity of mammographic screening also varies with image quality. To offset different kinds of variability and to standardize diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. This paper presents a one-class classification pipeline for the classification of breast cancer images into benign and malignant classes. Because of the sparse distribution of abnormal mammograms, the two-class classification problem is reduced to a one-class outlier identification problem. Trace transform, which is a generalization of the Radon transform, has been used to extract the features. Several new functionals specific to mammographic image analysis have been developed and implemented to yield clinically significant features. Classifiers such as the linear discriminant classifier, quadratic discriminant classifier, nearest mean classifier, support vector machine, and the Gaussian mixture model (GMM) were used. For automated diagnosis, the classification pipeline was tested on a set of 313 mammograms provided by the Singapore Anti-Tuberculosis Association CommHealth. A maximum accuracy rate of 92.48% has been obtained using GMMs.