In order to establish the correlation between bone structure and age, and information about age-related bone changes, it is necessary to study microstructural features of human bone. Traditionally, in bone biology and forensic science, the analysis if bone cross-sections has been carried out manually. Such a process is known to be slow, inefficient and prone to human error. Consequently, the results obtained so far have been unreliable.

In this paper we present a new approach to quantitative analysis of cross-sections of human bones using digital image processing techniques. We demonstrate that such a system is able to extract various bone features consistently and is capable of providing more reliable data and statistics for bones. Consequently, we will be able to correlate features of bone microstructure with age and possibly also with age related bone diseases such as osteoporosis. The development of knowledge-based computer vision systems for automated bone image analysis can now be considered feasible.