Mammography is one of the most important tools for the early detection of breast cancer typically through detection of characteristic masses and/or micro calcifications. Digital mammography has become commonplace in recent years. High quality mammogram images are large in size, providing high-resolution data. Estimates of the false negative rate for cancers in mammography are approximately 10%–30%. This may be due to observation error, but more frequently it is because the cancer is hidden by other dense tissue in the breast and even after retrospective review of the mammogram, cannot be seen. In this study, we report on the results of novel image processing algorithms that will enhance the images providing decision support to reading physicians. Techniques such as Butterworth high pass filtering and Gabor filters will be applied to enhance images; followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI, which will be used to classify the ROIs as either masses or non-masses. Among the statistical methods most used for the characterization of textures, the co-occurrence matrix makes it possible to determine the frequency of appearance of two pixels separated by a distance, at an angle from the horizontal. This matrix contains a very large amount of information that is complex. Therefore, it is not used directly but through measurements known as indices of texture such as average, variance, energy, contrast, correlation, normalized correlation and entropy.