Osteoporosis is a disease characterized by reduction in bone mass and micro-structure, causing high risk to fragility fractures. the gold standard diagnostic technique for osteoporosis, dual energy x-ray absorptiometry (dxa), is expensive and not widely available in india. we developed a low cost prescreening tool for early diagnosis of osteoporosis using hand and wrist radiographs. automated segmentation method for extraction of third metacarpal bone and distal radius is developed. cortical radiogrammetry of third metacarpal bone and texture analysis of distal radius is done and the most significant features are used to train classifiers. the prescreening tool is validated using 138 subjects from indian sample population. the segmentation method shows detection accuracy of 89% and 93.5% for the third metacarpal bone shaft and distal radius, respectively. the trained artificial neural network (ann) classifier achieves the best test accuracy of over 90.0%. a linear regression model shows a significant correlation of 0.671 with dxa. a novel low cost technique to measure cortical bone volume using three dimensional (3d) reconstruction of metacarpal bone is also developed from hand radiographs in three views. projections of the 3d reconstructed models are compared with manually segmented x-ray images of 20 subjects. the mean error percentage in combined cortical thickness is 11.2%. the prescreening tool is a promising technique to identify people with low bone mass and will enable mass screening of people in primary health centres in non-urban areas where dxa is unavailable. this work is done in collaboration with kasturba medical college (kmc) hospital, mangalore, india and university hospital of geneva, switzerland. the study protocol was approved by the institutional ethics committee, kmc hospital, mangalore, manipal academy of higher education, india.