Trabecular bone microarchitecture cannot be routinely evaluated. We have developed and validated a fractal analysis of trabecular bone texture on calcaneus radiographs. The aim of this work was to evaluate the ability of the fractal analysis to discriminate a group of 39 postmenopausal women with osteoporotic (OP) vertebral crush fractures (68.0 +/- 10.8 years) from an age-matched control group of 39 women (68.0 +/- 10.7 years). The value of the fractal analysis was compared with the value of the femoral neck bone mineral density (FNBMD) and trochanteric bone mineral density (TRBMD). The result is expressed by the parameter Hmean (Hmean = 2 - fractal dimension). Hmean value was 0.691 +/- 0.050 in the OP group versus 0.739 +/- 0.024 in the controls, while FNBMD was 0.598 +/- 0.113 g/cm2 versus 0.645 +/- 0.109 g/cm2 and TRBMD was 0.512 +/- 0.108 g/cm2 versus 0.594 +/- 0.106 g/cm2 respectively. The statistical significance of the Hmean test (p < 0.0001) was higher than for FNBMD (p < 0.05) and for TRBMD (p = 0.0004). We used a receiver operating characteristic (ROC) curve to check this superiority. The area under the ROC curve was 0.824 for Hmean, 0.633 for FNBMD and 0.727 for TRBMD. This superiority of the Hmean ROC curve was statistically significant versus FNBMD, but not versus TRBMD. In a second analysis, we studied the subgroups of OP patients and controls with overlapping FNBMD or TRBMD values to check whether the fractal dimension test could be discriminant in these subgroups. Significant statistical differences were found for Hmean between OP patients and controls in the overlapping subgroup for FNBMD or TRBMD (respectively p = 0.006 and p < 0.02). These data confirm that the fractal analysis of texture on calcaneus radiographs is able to discriminate OP patients with vertebral crush fracture from controls. This discrimination was stronger than that obtained by FNBMD or TRBMD alone. It was also present when we compared subgroups with overlapping values of FNBMD or TRBMD.

The purpose of this work was to understand how fractal dimension of two-dimensional (2D) trabecular bone projection images could be related to three-dimensional (3D) trabecular bone properties such as porosity or connectivity. Two alteration processes were applied to trabecular bone images obtained by magnetic resonance imaging: a trabeculae dilation process and a trabeculae removal process. The trabeculae dilation process was applied from the 3D skeleton graph to the 3D initial structure with constant connectivity. The trabeculae removal process was applied from the initial structure to an altered structure having 99% of porosity, in which both porosity and connectivity were modified during this second process. Gray-level projection images of each of the altered structures were simply obtained by summation of voxels, and fractal dimension (D f ) was calculated. Porosity () and connectivity per unit volume (C v ) were calculated from the 3D structure. Significant relationships were found between D f , , and C v . D f values increased when porosity increased (dilation and removal processes) and when connectivity decreased (only removal process). These variations were in accordance with all previous clinical studies, suggesting that fractal evaluation of trabecular bone projection has real meaning in terms of porosity and connectivity of the 3D architecture. Furthermore, there was a statistically significant linear dependence between D f and C v when remained constant. Porosity is directly related to bone mineral density and fractal dimension can be easily evaluated in clinical routine. These two parameters could be associated to evaluate the connectivity of the structure. (J Bone Miner Res 2000;15:691-699)

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Bone density is not the unique factor conditioning bone strength. Trabecular bone microarchitecture also plays an important role. We have developed a fractal evaluation of trabecular bone microarchitecture on calcaneus radiographs. Fractal models may provide a single numeric evaluation (the fractal dimension) of such complex structures. Our evaluation results from an analysis of images with a varying range of gray levels, without binarization of the image. It is based on the fractional brownian motion model, or more precisely on the analysis of its increment, the fractional gaussian noise (FGN). The use of this model may be considered validated if two conditions are fulfilled: the gaussian repartition and the self-similarity of our data. The gaussian repartition of intermediate lines of these images was tested on a sample of 32,800 lines from 82 images. Following a chi-square goodness-of-fit test, it was checked in 86% of these lines for alpha = 0.01. The self-similarity was tested on 20 images by two estimators, the variance method of Pentland and the spectrum method of Fourier. Self-similarity is defined by lined-up points in a log-log plot of the FGN spectrum or of the variance as a function of the lag. We found two self-similarity areas between scales of analysis ranging from 105 to 420 microns, then above 900 microns, where linear regression produced high mean correlation coefficients (r > or = 0.97). Following this validation, we studied the reproducibility of this new technique. Intra- and interobserver reproducibility, influence of transferring the region of interest, and long-term reproducibility were assessed and given CV of 0.61 +/- 0.15, 0.68 +/- 0.47, 0.53 +/- 0.16, and 2.07 +/- 0.84%, respectively. These data have allowed us to validate the use of this fractal model by checking the fractal organization of our radiographic images analyzed by the model. The good reproducibility of successive x-rays in the same subject allows us to undertake population studies and to envisage longitudinal series.