presented a study of contourlet based mammography mass classification using support vector
machine (SVM). In their study, a set of statistical properties of contourlet coefficients from 4 decomposition levels, cooccurrence matrix features and geometrical features is used as feature vector of ROI. Genetic algorithm was used for feature selection based on neural network pattern classification. They concluded that the contourlet features offer an improvement of the classification process. presented a study of mammogram classification based on curvelet transform. A fractional amount of the biggest coefficients from each decomposition level is used as feature vector. They proved that multiresolution based analysis achieved interesting results.