Knee X-Ray Image Analysis Method for Automated Detection of Osteoarthritis
Citations Over TimeTop 10% of 2008 papers
Abstract
We describe a method for automated detection of radiographic osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence (KL) classification grades, which correspond to the different stages of OA severity. The classifier was built using manually classified X-rays, representing the first four KL grades (normal, doubtful, minimal, and moderate). Image analysis is performed by first identifying a set of image content descriptors and image transforms that are informative for the detection of OA in the X-rays and assigning weights to these image features using Fisher scores. Then, a simple weighted nearest neighbor rule is used in order to predict the KL grade to which a given test X-ray sample belongs. The dataset used in the experiment contained 350 X-ray images classified manually by their KL grades. Experimental results show that moderate OA (KL grade 3) and minimal OA (KL grade 2) can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively. Doubtful OA (KL grade 1) was detected automatically with a much lower accuracy of 57%. The source code developed and used in this study is available for free download at www.openmicroscopy.org.
Related Papers
- → Osteoarthritis prevention(2011)64 cited
- → Hand joints without radiographic osteoarthritis maintain their joint space width over 4 years despite what’s happening elsewhere in the hand(2023)1 cited
- An Atlas of Osteoarthritis(2001)
- Age and osteoarthritis: are AGEs the link?(2012)
- Hand osteoarthritis : natural course and determinants of outcome(2013)