Osteoporosis is a common bone disease that increases risk of low-trauma fractures associated with substantial morbidity, mortality, and financial costs. Clinically, osteoporosis is defined by low bone mineral density (BMD). BMD explains approximately 60-70% of the variance in bone strength. The remainder is due to the cumulative and synergistic effects of other factors, including trabecular and cortical bone micro-architecture. In vivo quantitative characterization of trabecular bone (TB) micro-architecture with high accuracy, reproducibility, and sensitivity to bone strength will improve our understanding of bone loss mechanisms and etiologies benefitting osteoporotic diagnostics and treatment monitoring processes. The overall aim of the Ph.D. research is to design, develop and evaluate new 3-D imaging processing algorithms to characterize the quality of TB micro-architectural in terms of topology, orientation, thickness and spacing, and to move the new technology from investigational research into the clinical arena. Two algorithms regarding to this purpose were developed and validated in detail - (1) star-line-based TB thickness and marrow spacing computation algorithm, and (2) tensor scale (t-scale) based TB topology and orientation computation algorithm. The TB thickness and marrow spacing algorithm utilizes a star-line tracing technique that effectively accounts for partial voluming effects of in vivo imaging with voxel size comparable to TB thickness and also avoids the problem of digitization associated with conventional algorithms. Accuracy of the method was examined on computer-generated phantom images while the robustness of the method was evaluated on human ankle specimens in terms of stability across a wide range of resolutions, repeat scan reproducibility under in vivo condition, and correlation between thickness values computed at ex vivo and in vivo resolutions. Also, the sensitivity of the method was examined by its ability to predict bone strength of cadaveric specimens. Finally, the method was evaluated in an in vivo human study involving forty healthy young-adult volunteers and ten athletes. The t-scale based TB topology and orientation computation algorithm provides measures characterizing individual trabeculae on the continuum between perfect plate and perfect rod as well as individual trabecular orientation. Similar to the TB thickness and marrow spacing computation algorithm, accuracy was examined on computer-generated phantoms while robustness of the algorithm across ex vivo and in vivo resolution, repeat scan reproducibility, and the sensitivity to experimental mechanical bone strength were evaluated in a cadaveric ankle study. And the application of the algorithm was evaluated in a human study involving forty healthy young-adult volunteers and ten patients with SSRI treatment. Beside these two algorithms, an image thresholding algorithm based on the class uncertainty theory is developed to segment TB structure in CT images. Although the algorithm was developed for this specific application, it also works effectively for general 2-D and 3-D images. Moreover, the class uncertainty theory can be utilized as adaptive information in more sophisticated image processing algorithms such as Snakes, ASMs and graph search.