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A computer-based image analysis method for assessing the severity of hip joint osteoarthritis

机译:一种用于评估髋关节骨关节炎严重程度的基于计算机的图像分析方法

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摘要

A computer-based image analysis method was developed for assessing the severity of hip osteoarthritis (OA). Eighteen pelvic radiographs of patients with verified unilateral hip OA, were digitized and enhanced employing custom developed software. Two ROIs corresponding to osteoarthritic and contralateral-physiological radiographic Hip Joint Spaces (HJSs) were determined on each radiograph. Textural features were extracted from the HJS-ROIs utilizing the run-length matrices and Laws textural measures. A k-Nearest Neighbour based hierarchical tree structure was designed for classifying hips into three OA severity categories labeled as "Normal", "Mild/Moderate", and "Severe". Employing the run-length features, the overall classification accuracy of the hierarchical tree structure was 86.1%. The utilization of Laws' textural measures improved the system classification performance, providing an overall classification accuracy of 94.4%. The proposed method maybe of value to physicians in assessing the severity of hip OA.
机译:开发了一种基于计算机的图像分析方法,用于评估髋骨关节炎(OA)的严重程度。使用定制开发的软件对经过验证的单侧髋骨OA患者的18例骨盆X线照片进行数字化和增强。在每张X线片上确定了两个与骨关节炎和对侧生理X线摄影髋关节间隙(HJS)相对应的ROI。使用游程矩阵和Laws纹理度量从HJS-ROI中提取纹理特征。设计了一种基于k最近邻的分层树结构,用于将臀部分为三个OA严重性类别,分别标记为“正常”,“轻度/中度”和“严重”。利用游程特征,分层树结构的整体分类精度为86.1%。使用Laws的纹理度量提高了系统分类性能,总体分类精度为94.4%。所提出的方法可能对医师评估髋骨OA的严重性有价值。

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