Jean-Pierre Hamelin

Fractal dimension of trabecular bone: comparison of three histomorphometric computed techniques for measuring the architectural two‐dimensional complexity

Trabecular bone has been reported as having two‐dimensional (2‐D) fractal characteristics at the histological level, a finding correlated with biomechanical properties. However, several fractal dimensions (D) are known and computational ways to obtain them vary considerably. This study compared three algorithms on the same series of bone biopsies, to obtain the Kolmogorov, Minkowski–Bouligand, and mass‐radius fractal dimensions. The relationships with histomorphometric descriptors of the 2‐D trabecular architecture were investigated. Bone biopsies were obtained from 148 osteoporotic male patients. Bone volume (BV/TV), trabecular characteristics (Tb.N, Tb.Sp, Tb.Th), strut analysis, star volumes (marrow spaces and trabeculae), inter‐connectivity index, and Euler–Poincaré number were computed. The box‐counting method was used to obtain the Kolmogorov dimension (Dk), the dilatation method for the Minkowski–Bouligand dimension (DMB), and the sandbox for the mass‐radius dimension (DMR) and lacunarity (L). Logarithmic relationships were observed between BV/TV and the fractal dimensions. The best correlation was obtained with DMR and the lowest with DMB. Lacunarity was correlated with descriptors of the marrow cavities (ICI, star volume, Tb.Sp). Linear relationships were observed among the three fractal techniques which appeared highly correlated. A cluster analysis of all histomorphometric parameters provided a tree with three groups of descriptors: for trabeculae (Tb.Th, strut); for marrow cavities (Euler, ICI, Tb.Sp, star volume, L); and for the complexity of the network (Tb.N and the three Ds). A sole fractal dimension cannot be used instead of the classic 2‐D descriptors of architecture; D rather reflects the complexity of branching trabeculae. Computation time is also an important determinant when choosing one of these methods. Copyright