  The relationship between knee osteoarthritis progression and changes in
tibial bone structure has long been recognized and various texture descriptors
have been proposed to detect early osteoarthritis (OA) from radiographs. This
work aims to investigate (1) femoral textures as an OA indicator and (2) the
potential of entropy as a computationally efficient alternative to established
texture 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 employ
statistical and machine learning methods to evaluate feature combinations.
  Based on 153 high-resolution radiographs, our results identify medial femur
as an effective univariate descriptor, with significance comparable to medial
tibia. Entropy is shown to contribute to classification performance. A linear
five-feature classifier combining femur, entropic and standard texture
descriptors, achieves AUC of 0.85, outperforming the state-of-the-art by
roughly 0.1.
