Analysis of Trabecular Structure in Radiographic Bone Images Using Empirical Mode Decomposition and Extreme Learning Machine

Abstract

Biomechanical function is a primary concern in musculo-skeletal supporting system in our body. The functional adaptation of femur bone to the mechanical environment is an important component of bonemechanics research. The strength and architecture of these structures in femur bones are routinely analyzed by varied image based methods for diagnosis and monitoring of osteoporosis like metabolic disorders. In this work, an attempt has been made for investiagtion of trabecular femur bone architecture using spatial frequency decomposition and neural networks. Conventional radiographic femur bone images are recorded using standard protocols in this analysis. The compressive and tensile regions in the femur bone images are delineated using preprocessing procedures. The delineated images are analyzed using Fast and Adaptive Bi-dimensional Empirical Mode Decomposition (FABEMD) to quantify pattern heterogeneity and anisotropy of trabecular bone structure. The characteristic feature vectors are extracted and further subjected to classification using Extreme Learning Machine (ELM). Results show that FABEMD analysis combined with ELM could differentiate normal and abnormal images.