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Proceedings Paper

Combination of lateral and PA view radiographs to study development of knee OA and associated pain
Author(s): Luca Minciullo; Jessie Thomson; Timothy F. Cootes
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Paper Abstract

Knee Osteoarthritis (OA) is the most common form of arthritis, affecting millions of people around the world. The effects of the disease have been studied using the shape and texture features of bones in PosteriorAnterior (PA) and Lateral radiographs separately. In this work we compare the utility of features from each view, and evaluate whether combining features from both is advantageous. We built a fully automated system to independently locate landmark points in both radiographic images using Random Forest Constrained Local Models. We extracted discriminative features from the two bony outlines using Appearance Models. The features were used to train Random Forest classifiers to solve three specific tasks: (i) OA classification, distinguishing patients with structural signs of OA from the others; (ii) predicting future onset of the disease and (iii) predicting which patients with no current pain will have a positive pain score later in a follow-up visit. Using a subset of the MOST dataset we show that the PA view has more discriminative features to classify and predict OA, while the lateral view contains features that achieve better performance in predicting pain, and that combining the features from both views gives a small improvement in accuracy of the classification compared to the individual views.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013411 (3 March 2017); doi: 10.1117/12.2254295
Show Author Affiliations
Luca Minciullo, The Univ. of Manchester (United Kingdom)
Jessie Thomson, The Univ. of Manchester (United Kingdom)
Timothy F. Cootes, The Univ. of Manchester (United Kingdom)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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