The proposed method presents a new classification approach to microcalcification detection in mammograms using morphology, Contourlet Transform and Artificial Neural Network. Early detection of breast cancer is possible by enhancing microcalcification features obtained using morphology and singularities of Contourlet Transform. The significant edge information indicating the relevant features in various decomposition levels are preserved while removing the replique hublot big bang artifacts. These features are utilized to detect microcalcifications by classification employing the Back Propagation Neural Network. Target to background contrast ratio, Contrast and Peak Signal to Noise ratio are considered for performance evaluation of the enhancement algorithm. The accuracy of the classification algorithm is 95% panerai replica italia. The mini-MIAS mammographic database is employed for testing the accuracy of the proposed method and the results are promising.