A composite framework for segregating x-rays of osteoporotic cases from healthy controls

The assessment of osteoporotic subjects from X-ray images poses a significant challenge for pattern recognition and medical diagnostic applications. Textured images of bone micro-architecture of osteoporotic and healthy subjects exhibit high degree of similarity hence amplifying difficulty of classifying such textures. This research is focused on exploring different texture based methods to segregate osteoporotic from healthy controls. We enacted set of well evaluated preprocessing model to enhance the prospects of drawing a distinct line between two classes while exercising diverse texture analysis approaches including Grey Level Co-occurrence Matrix (GLCM), two-dimensional and one-dimensional Local Binary Patterns. Finally we propose a hybrid technique to attain an enhanced class distinction. Consequently experiments were conducted on two populations of osteoporotic patients and controls, with comparative analysis of the results.