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

• URL: http://arxiv.org/abs/2004.01029v1
• Date: Thu, 2 Apr 2020 14:33:03 GMT
• Title: Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens
• Authors: Titas De
• Abstract summary: 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. This study proposes a new method to predict the bone strength of proximal femur specimens from quantitative multi-detector computer tomography (MDCT) images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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