Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
505301 | Computers in Biology and Medicine | 2012 | 8 Pages |
Abstract
We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker. The performance of the developed marker reached a generalization area-under-the-ROC (AUC) of 0.82, which is higher than the established cartilage markers known to relate to the OA diagnosis.
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Authors
Joselene Marques, Rabia Granlund, Martin Lillholm, Paola C. Pettersen, Erik B. Dam,