Ms. Jessie Thomson Profile

Jessie Thomson

at Univ of Manchester

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Proceedings Article | 3 March 2017 Presentation + Paper

Combination of lateral and PA view radiographs to study development of knee OA and associated pain

Luca Minciullo, Jessie Thomson, Timothy Cootes

Proceedings Volume 10134, 1013411 (2017) https://doi.org/10.1117/12.2254295

KEYWORDS: Radiography, Feature extraction, Bone, Thermal modeling, Statistical modeling, Mathematical modeling, Image segmentation, Sensors, Image classification, Analytical research

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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.

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