Osteoarthritis (OA) is a common musculoskelet al 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.
The measurement of joint space width (JSW) in hand x-ray images of patients suffering from Rheumatoid Arthritis (RA) is a time consuming task for radiologists. Manual assessment lacks accuracy and is observer-dependent, which hinders an accurate evaluation of joint degeneration in early diagnosis and follow-up studies. Automatic analysis of the JSW is crucial with regard to standardization, sensitivity, and reproducibility. In this paper, we focus on both joint location and joint margin detection. For the evaluation, five hand radiographs from RA patients, in which the joints have been manually delineated, are used. All finger joints are located correctly with margins differing 0.1 mm on average from the manual delineation.