Application of fuzzy skeletonization ot quantitatively assess trabecular bone micro-architecture

Adult bone diseases, especially osteoporosis, lead to increased risk of fracture associated with substantial morbidity, mortality, and financial costs. Clinically, osteoporosis is defined by low bone mineral density; however, increasing evidence suggests that the micro-architectural quality of trabecular bone (TB) is an important determinant of bone strength and fracture risk. Skeletonization plays an important role providing a compact representation of TB network that allows computation of several quantitative parameters relating to TB micro-architecture. Literature of three-dimensional skeletonization is quite matured for binary digital objects. However, the challenges of skeletonization for fuzzy objects are mostly unanswered. Here, an algorithm for fuzzy skeletonization is presented using fuzzy grassfire propagation and a branch-level noise pruning strategy and, finally, its application to TB micro-architectural assessment is investigated. Specifically, the fuzzy skeletonization algorithm is applied to compute TB plateness, plate/rod ratio, thickness, and spacing. Finally, the effectiveness of these measures to predict experimental bone strength is investigated on twelve cadaveric specimens and the results are encouraging with the R² value of linear correlation with bone strength being as high as 0.93, 0.88, 0.85 and 0.86, respectively.