The recognition of microcalcifications and masses from digital mammographic images are important to aid the detection of breast cancer. In this paper, we applied morphological techniques to extract the embedded structures from the images for subsequent analysis. A mammographic phantom was created with embedded structures such as micronodules, nodules and fibrils. For the preprocessing techniques, intensity transformation of gray scale was applied to the image. The structures of the image were enhanced and segmented using dilation for a morphological operation with morphological closing. Next, low pass Gaussian filter was applied to the image to smooth and reduce noises. It was found that our method improved the detection of microcalcifications and masses with high Peak Signal To Noise Ratio (PSNR).