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The relationship between knee osteoarthritis progression and changes intibial bone structure has long been recognized and various texture descriptorshave been proposed to detect early osteoarthritis (OA) from radiographs. Thiswork aims to investigate (1) femoral textures as an OA indicator and (2) thepotential of entropy as a computationally efficient alternative to establishedtexture descriptors.  We design a robust semi-automatically placed layout for regions of interest(ROI), compute the Hurst coefficient and the entropy in each ROI, and employstatistical and machine learning methods to evaluate feature combinations.  Based on 153 high-resolution radiographs, our results identify medial femuras an effective univariate descriptor, with significance comparable to medialtibia. Entropy is shown to contribute to classification performance. A linearfive-feature classifier combining femur, entropic and standard texturedescriptors, achieves AUC of 0.85, outperforming the state-of-the-art byroughly 0.1.

Femoral ROIs and Entropy for Texture-based Detection of Osteoarthritis from High-Resolution Knee Radiographs