Advertisement
Published in:
2015 | OriginalPaper | Chapter
Automated Shape and Texture Analysis for Detection of Osteoarthritis from Radiographs of the Knee
Authors : Jessie Thomson, Terence O’Neill, David Felson, Tim Cootes
Published in: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Osteoarthritis (OA) is considered to be one of the leading causes of disability, however clinical detection relies heavily on subjective experience to condense the continuous features into discrete grades. We present a fully automated method to standardise the measurement of OA features in the knee used to diagnose disease grade. Our approach combines features derived from both bone shape (obtained from an automated bone segmentation system) and image texture in the tibia. A simple weighted sum of the outputs of two Random Forest classifiers (one trained on shape features, the other on texture features) is sufficient to improve performance over either method on its own. We also demonstrate that Random Forests trained on simple pixel ratio features are as effective as the best previously reported texture measures on this task. We demonstrate the performance of the system on 500 knee radiographs from the OAI study.