  Bone fragility and fracture caused by osteoporosis or injury are prevalent in
adults over the age of 50 and can reduce their quality of life. Hence,
predicting the biomechanical bone strength, specifically of the proximal femur,
through non-invasive imaging-based methods is an important goal for the
diagnosis of Osteoporosis as well as estimating fracture risk. Dual X-ray
absorptiometry (DXA) has been used as a standard clinical procedure for
assessment and diagnosis of bone strength and osteoporosis through bone mineral
density (BMD) measurements. However, previous studies have shown that
quantitative computer tomography (QCT) can be more sensitive and specific to
trabecular bone characterization because it reduces the overlap effects and
interferences from the surrounding soft tissue and cortical shell.
  This study proposes a new method to predict the bone strength of proximal
femur specimens from quantitative multi-detector computer tomography (MDCT)
images. Texture analysis methods such as conventional statistical moments (BMD
mean), Isotropic Minkowski Functionals (IMF) and Anisotropic Minkowski
Functionals (AMF) are used to quantify BMD properties of the trabecular bone
micro-architecture. Combinations of these extracted features are then used to
predict the biomechanical strength of the femur specimens using sophisticated
machine learning techniques such as multiregression (MultiReg) and support
vector regression with linear kernel (SVRlin). The prediction performance
achieved with these feature sets is compared to the standard approach that uses
the mean BMD of the specimens and multiregression models using root mean square
error (RMSE).
