Osteoporosis is a metabolic bone disorder characterized by low bone mass, degradation of bone micro-architecture and susceptibility to fracture. It is a growing major health concern across the world, especially in the elderly population. Osteoporosis can cause hip or spinal fractures that may lead to high morbidity and socio-economic burden. Therefore, there is a need for early diagnosis of osteoporosis and prediction of fragility fracture risk. In this paper, state-of-the-art and recent advances in imaging techniques for diagnosis of osteoporosis and fracture risk assessment have been explored. A review of segmentation methods used to segment the regions of interest and texture analysis methods used for classification of healthy and osteoporotic subjects are also presented. Furthermore, challenges posed by the current diagnostic tools have been studied and feasible solutions to circumvent the limitations are discussed. Early diagnosis of osteoporosis and prediction of fracture risk requires the development of highly precise and accurate low-cost diagnostic techniques that would help the elderly population in low economies.

An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis.

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Abstract-In this work, we develop image processing and computer vision techniques for visually tracking a tennis ball, in 3D, on a court instrumented with multiple low-cost IP cameras. The technique first extracts 2D ball track data from each camera view, using object tracking methods. Next, an automatic featurebased video synchronization method is applied. This technique uses both the extracted 2D ball information from two or more camera views, plus camera calibration information. Then, in order to find 3D trajectory, the temporal 3D locations of the ball is estimated using triangulation of correspondent 2D locations obtained from automatically synchronized videos. Furthermore, we also incorporate a physics-based trajectory model into the system to improve the continuity of the tracked 3D ball during times when no two cameras have overlapping views of the ball location. The resultant 3D ball tracks are then visualized in a virtual 3D graphical environment. Finally, we quantify the accuracy of our system in terms of reprojection error.