CAD FOR DETECTION OF MICROCALCIFICATION AND CLASSIFICATION IN MAMMOGRAMS

In this study, computer aided diagnosis (CAD) is developed to detect microcalficication cluster which is one of the important radiological findings of breast cancer diagnosis and classificiation. For this purpose, image processing and pattern recognition algorithms are applied on mamographic images. To make microcalcifications more visible wavelet transform and nonsubsampled contourlet transform (NSCT) methods are used for image enhancement. Their performances are compared. 52 features are extracted from the enhanced images. To reduce the dimension of the feature space, linear discriminant analysis is applied. It is observed that nonsubsampled contourlet transform outperforms the wavelet transform. Microcalcification clusters were classified by using support vector machine (SVM) by 94,6% correct rate.