Classification of Mammogram Images using GLCM and Trace Transform Functionals - MATLAB PROJECTS CODE
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. It is difficult for radiologists to identify the masses on a mammogram because they are surrounded by complicated tissues. Computer-aided detection (CADe) systems have been developed to aid radiologists in detecting mammographic lesions which may indicate the presence of breast cancer.
Trace transform, which is a generalization of the radon transform and GLCM(grey-level co-occurrence matrix) together has been used to extract the features .GLCM compute four cooccurence matrices with one pixel distance in four directions: left diagonal, right diagonal, vertical and horizontal.Four statistics can be calculated by describing the image texture i.e energy , contrast , correlation , homogenity are four texture features. Gaussian classifier is used in the classification of mammogram images into normal, abnormal, benign and malignant classes.