IEEE Trans Biomed Eng. 2013 Oct;60(10):2896-903. doi: 10.1109/TBME.2013.2266325. Epub 2013 Jun 5.
Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: 0.94 ±0.08 ) and "Euler characteristic" (AUC: 0.94 ±0.07 ), and GLCM-derived feature "Correlation" (AUC: 0.93 ±0.07). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.
先前已经证明，通过相差衬度成像 X 射线计算机断层扫描（PCI-CT）可以可视化离体人髌骨软骨基质。这些研究揭示了骨关节炎引起的软骨细胞在放射状区域的排列变化。本研究探讨了纹理分析在存在和不存在骨关节炎损伤的情况下，对软骨细胞模式进行特征描述的应用。从离体人髌骨软骨标本的 PCI-CT 图像中注释的 842 个感兴趣区域（ROI）中提取了来自 Minkowski 函数（MF）和灰度共生矩阵（GLCM）的纹理特征。随后，使用支持向量回归的机器学习任务，将这些纹理特征用于分类 ROI 为健康或骨关节炎；使用接收器操作特征曲线下的面积（AUC）评估分类性能。MF 特征周长（AUC：0.94 ±0.08）和“欧拉特征”（AUC：0.94 ±0.07）以及 GLCM 衍生特征“相关性”（AUC：0.93 ±0.07）表现出最佳的分类性能。这些结果表明，这种纹理特征可以提供软骨基质中软骨细胞排列的详细特征描述，能够以高精度对软骨进行健康或骨关节炎分类。
