Fractures of the proximal femur represent the worst complication in osteoporosis with a
mortality rate of up to 50% during the first post-traumatic year.
Bone mineral density (BMD) as obtained from dual energy x-ray absorptiometry (DXA) is a
good predictor of fracture risk. However, there is a considerable overlap in the BMD-results
between individuals who have fractured and those who have not. As DXA uses highly
standardized radiographic projection images to obtain the densitometric information, it can be
postulated that these images contain much more information than just mineral density. Lately,
geometric dimensions, e.g. hip axis length (HAL) or femoral neck axis length (FNAL), are
considered in conjunction with BMD, which may allow to enhance the predictive potential of
bone mass measurements.
In recent studies we sucessfully introduced a novel methodology for topological analysis of
multi-dimensional graylevel datasets, that, for instance, allows to predict the ultimate
mechanical strength of femoral bone specimens. The new topolocial parameters are based on
the so called Minkowski Functionals (MF), which represent a set of topographical descriptors
that can be used universally. Since the DXA-images are multi-graylevel datasets in 2D
obtained in a standardized way, they are ideally suited to be processed by the new method.
In this study we introduce a novel algorithm to evaluate DXA-scans of the proximal femur
using quantitative image analysis procedures based on the MF in 2D. The analysis is
conducted in four defined regions of interest in analogy to the standard densitometric
evaluation. The objective is to provide a tool to identifiy individuals with critically reduced
mechanical competence of the hip. The result of the new method is compared with the
evaluation bone mineral density obtained by DXA, which - at present - is the clinical standard