This thesis deals with the detection of masses in mammographic images. Such masses are signs of breast cancer. The contribution of the thesis is the development of a new framework for the detection of masses by taking breast density into account. As a first step, Regions of Interests (ROIs) are detected in the image using templates containing a probabilistic contour shape obtained from training over an annotated set of masses. Firstly, PCA is performed over the training set, and subsequently the template is formed as an average of the gradient of eigenmasses weighted by the top eigenvalues. The training set is clustered into sub-sets of equal size so that PCA can be applied. The template can be deformed according to each eigenmass coefficient. The matching is formulated in a Bayesian framework, where the prior penalizes the deformation, and the likelihood requires template boundaries to agree with image edges in both position and tangents. In the second stage, the detected ROIs are classified into being false positives or true positives using 2DPCA, where the new training set now contains ROIs with masses and ROIs with normal tissue. Mass density is incorporated into the whole process by initially classifying the two training sets according to breast density. Methods for breast density estimation are also analyzed and proposed. The results are obtained using four different mammographic databases (three digitized and one digital). FROC and ROC analysis demonstrate the validity of our approach. The results show a better performance of the approach relative to competing methods. The false positive reduction stage improves the outcome significantly, and the breast density information provides also an improvement.