Computer aided diagnostic tool can aid the radiologist in the early detection of breast cancer. Even though mammography is considered to be the gold standard for breast cancer detection, it is limited by the spatial superposition of tissue. This limitation is the result of a using a two dimensional, (2D), representation of a three dimensional, (3D), structure. The limitation contributes to and results in misclassification of breast cancers. Tomosynthesis is a limited-angle 3D imaging device that overcomes this limitation by representing the breast structure with 3D volumetric data.This research, on tomosynthesis imaging, was a critical module of a larger research endeavor for the detection of breast cancer. Tomosynthesis is an emerging state-of-the-art 3D imaging technology. The purpose of this research was to develop a tomosynthesis based, efficient suspicious region segmentation, procedure for the breast to enhance the detection and diagnosis of breast cancer. The 3D breast volume is constructed to visualize the 3D structure of the breast region. Advanced image processing and analysis algorithms were developed to remove out-of-plane artifacts and increase the Signal Difference to Noise Ratio, (SDNR), of tomosynthetic images. Suspicious regions are extracted from the breast volume using efficient and robust clustering algorithms.A partial differential equation based non-linear diffusion method was modified to include the anisotropic nature of tomosynthesis data in order to filter out the out-of-plane artifacts, which are termed "tomosynthetic noise", and to smooth the in-plane noise. Fuzzy clustering algorithms were modified to include spatial domain information to segment suspicious regions. A significant improvement was observed, both qualitatively and quantitatively, in segmentation of the filtered data over the non-filtered data. The 3D segmentation system is robust enough to be used for statistical analysis of huge databases.