Breast cancer is one of the most important cause to mortality rate among women. Computer-Aided Detection (CAD) schemes have been developed as a tool in detecting early breast cancer. This can be an important tool in mammography since previous studies have been indicated that the detection of breast cancer can be increased up to 20% when assisted by a CAD scheme. One of the main stages of such process is thus the segmentation of structures of interest, as the suspect masses. However, when evaluating mammograms obtained from dense breasts, a CAD scheme efficacy can be very reduced due to the poor contrast of such type of image. This work attempts hence to this challenge, by describing a methodology for segmenting suspect masses in dense breast images as a part of a CAD scheme under development. This methodology is based on the Watershed transformation, which is combined with two other procedures – a histogram equalization, working as pre-processing for enhance images contrast, and a labeling procedure intended to reduce noise. Tests with a set of 252 regions of interest extracted from 130 digitized mammograms have registered a scheme sensibility of 92% with about 90% of specificity. These results are promising when applied to dense breast images, which can improve significantly the performance of a processing scheme for such type of cases in mammography.