A new diagnosis method of microcalcification is applied based on dual-tree complex wavelet transform.A set of texture features are extracted,including the wavelet based features and gray-level histogram based features.Combining the genetic algorithm to optimize the features,the neural network,support vector machine and KNN classifier are used to distinguish the benign and malignant microcalcifications.The results of the three different classifiers are analyzed,which show that KNN classifier get the best result and support vector outperformed neural network.Compared with the other two classifiers,KNN classifier is a non-training method which can save much training time.It directly computes the distances between samples,which can reduce the adverse effects caused by inefficient selection of features and minimize the error in classification.The KNN classifier depends on a small quantity of samples when kind deciding,which can solve the problem caused by the imbalance of samples.Compared to traditional wavelet basis,the shift invariance and high regularity of dual-tree complex wavelet make it more helpful for direction selection of image signal,and dual-tree complex wavelet transform will get limited redundancy and less amount of calculation.