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Bone fragility and fracture caused by osteoporosis or injury are prevalent inadults 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 thediagnosis of Osteoporosis as well as estimating fracture risk. Dual X-rayabsorptiometry (DXA) has been used as a standard clinical procedure forassessment and diagnosis of bone strength and osteoporosis through bone mineraldensity (BMD) measurements. However, previous studies have shown thatquantitative computer tomography (QCT) can be more sensitive and specific totrabecular bone characterization because it reduces the overlap effects andinterferences from the surrounding soft tissue and cortical shell.  This study proposes a new method to predict the bone strength of proximalfemur specimens from quantitative multi-detector computer tomography (MDCT)images. Texture analysis methods such as conventional statistical moments (BMDmean), Isotropic Minkowski Functionals (IMF) and Anisotropic MinkowskiFunctionals (AMF) are used to quantify BMD properties of the trabecular bonemicro-architecture. Combinations of these extracted features are then used topredict the biomechanical strength of the femur specimens using sophisticatedmachine learning techniques such as multiregression (MultiReg) and supportvector regression with linear kernel (SVRlin). The prediction performanceachieved with these feature sets is compared to the standard approach that usesthe mean BMD of the specimens and multiregression models using root mean squareerror (RMSE).

Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens