Medical assessment of bone health often uses quantitative computed tomography (QCT) scans and requires a reliable segmentation of bone geometry from surrounding tissue for a quick determination of bone mineral mass. Because of its shape and position in the body, the femur is one of the most challenging bones to investigate. In the current study we developed a new automated way to accurately evaluate both the shape and the mineral mass of cadaveric femora. The results were achieved through a series of steps including the segmentation of bone tissue from sets of QCT images, the estimation of the bone's outer surface, the calculation of the volume enclosed, and finally the evaluation of bone mineral mass in a user-defined region. We compared our algorithms results to results obtain by expert manual segmentation and results obtained using other published methods. This new method has the potential to be used in the clinic with patient QCT scans as a fast and reliable tool for diagnostics.