Breast cancer has become one of the most common cancers worldwide. It is also one of the leading causes of death in women. Early detection of breast cancer is the need of the hour as it offers increased treatment options, better survival chances and improved quality of life. The most economic technique is screening mammograms, to promote breast cancer identification at an early stage. However, image quality of screening mammograms is poor because they use low dose X-rays for image acquisition. Hence special attention is to be paid for enhancing the quality of screening mammograms.This paper proposes an enhancement strategy for screening mammograms based on Dual-Tree Complex Wavelet Transform (DTCWT).The DTCWT yields six directionally selective high pass sub-bands at +150,-150,+450,-450,+750,-750 on multiple scales.The reconstruction from the decomposed bands produce resultant image with enhancement in directional edges. Thus edge details are enhanced, which provides improved visualization of mass boundaries present inside the breast tissues.The experimental study on three publicly available mammogram datasets using the proposed DTCWT has the lowest Absolute Mean Brightness Error (AMBE) and Root Mean square Error (RMSE). Also, this method has higher values for Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR), which are indicators of overall image quality improvement and preservation of structural similarity. The proposed method offers PSNR values of 54.3612, 46.043, 48.8719 and SSIM is 0.9977, 0.9869, 0.9916 for the MIAS, DDSM and Inbreast datasets respectively.The proposed DTCWT based mammogram enhancement can be used as an efficient preprocessing technique in a computer-aided diagnosis system to detect breast cancer in its early stage. The comparison of the proposed method with many of the recent algorithms depicts the overall dominance of its performance, with the minimum error and good signal enhancement.