  Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed
from radiographic assessment after clinical examination. However, a visual
evaluation made by a practitioner suffers from subjectivity and is highly
dependent on the experience. Computer-aided diagnostics (CAD) could improve the
objectivity of knee radiographic examination. The first essential step of knee
OA CAD is to automatically localize the joint area. However, according to the
literature this task itself remains challenging. The aim of this study was to
develop novel and computationally efficient method to tackle the issue. Here,
three different datasets of knee radiographs were used (n = 473/93/77) to
validate the overall performance of the method. Our pipeline consists of two
parts: anatomically-based joint area proposal and their evaluation using
Histogram of Oriented Gradients and the pre-trained Support Vector Machine
classifier scores. The obtained results for the used datasets show the mean
intersection over the union equal to: 0.84, 0.79 and 0.78. Using a high-end
computer, the method allows to automatically annotate conventional knee
radiographs within 14-16ms and high resolution ones within 170ms. Our results
demonstrate that the developed method is suitable for large-scale analyses.
