Abstract
A pattern recognition system is proposed for the characterization of hip osteoarthritis (OA) severity. Sixty-four (64) hips, corresponding to 32 unilateral and bilateral OA patients were studied. Employing the Kellgren and Lawrence scale, hips were grouped into three OA severity categories: “Normal”, “Mild/Moderate”, and “Severe”. Utilizing custom-developed software, 64 ROIs, corresponding to patients’ radiographic Hip Joint Spaces (HJSs), were determined on digitized radiographs. A Probabilistic Neural Network classifier was designed employing morphological descriptors of the HJS-ROIs. The classifier discriminated successfully between (i) normal and OA hips (92.2% accuracy) and (ii) hips of “Mild/Moderate” OA and of “Severe” OA (91.3% accuracy). The proposed system could contribute in assessing hip OA severity.
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