Snekhalatha U, Rajalakshmi T, Gopikrishnan M, Gupta Nilkantha
1 Department of Biomedical Engineering, Faculty of Engineering and Technology, SRM University, Chennai, India.
2 Center for Environmental Nuclear Research, SRM University, Chennai, India.
Proc Inst Mech Eng H. 2017 Dec;231(12):1178-1187. doi: 10.1177/0954411917737329. Epub 2017 Oct 27.
The aim and objectives of the study are as follows: (1) to perform automated segmentation of knee X-ray images using fast greedy snake algorithm and feature extraction using gray level co-occurrence matrix method, (2) to implement automated segmentation of knee thermal image using RGB segmentation method and (3) to compare the features extracted from the segmented knee region of X-ray and thermal images in rheumatoid arthritis patients using a biochemical method as standard. In all, 30 rheumatoid arthritis patients and 30 age- and sex-matched healthy volunteers were included in the study. X-ray and thermography images of knee regions were acquired, and biochemical tests were carried out subsequently. The X-ray images were segmented using fast greedy snake algorithm, and feature extractions were performed using gray level co-occurrence matrix method. The thermal image was segmented using RGB-based segmentation method and statistical features were extracted. Statistical features extracted after segmentation from X-ray and thermal imaging of knee region were correlated with the standard biochemical parameters. The erythrocyte sedimentation rate shows statistically significant correlations (p < 0.01) with the X-ray parameters such as joint space width and % combined cortical thickness. The skin surface temperature measured from knee region of thermal imaging was highly correlated with erythrocyte sedimentation rate. Among all the extracted features namely mean, variance, energy, homogeneity and difference entropy depict statistically significant percentage differences between the rheumatoid arthritis and healthy subjects. From this study, it was observed that thermal infrared imaging technique serves as a potential tool in the evaluation of rheumatoid arthritis at an earlier stage compared to radiography. Hence, it was predicted that thermal imaging method has a competency in the diagnosis of rheumatoid arthritis by automated segmentation methods.
（1）使用快速贪婪蛇算法对膝关节X线图像进行自动分割，并使用灰度共生矩阵法进行特征提取；（2）使用RGB分割法对膝关节热图像进行自动分割；（3）以生化方法为标准，比较类风湿性关节炎患者X线图像和热图像分割后的膝关节区域提取的特征。本研究共纳入30例类风湿性关节炎患者和30例年龄及性别匹配的健康志愿者。采集膝关节区域的X线和热成像图像，随后进行生化检测。使用快速贪婪蛇算法对X线图像进行分割，并使用灰度共生矩阵法进行特征提取。使用基于RGB的分割法对热图像进行分割并提取统计特征。膝关节区域X线和热成像分割后提取的统计特征与标准生化参数相关。红细胞沉降率与关节间隙宽度和联合皮质厚度百分比等X线参数具有统计学显著相关性（p < 0.01）。热成像膝关节区域测量的皮肤表面温度与红细胞沉降率高度相关。在所有提取的特征中，即均值、方差、能量、均匀性和差异熵，类风湿性关节炎患者与健康受试者之间呈现出统计学显著的百分比差异。从本研究中观察到，与放射照相术相比，热红外成像技术在类风湿性关节炎的早期评估中是一种潜在工具。因此，预计热成像方法通过自动分割方法在类风湿性关节炎的诊断中具有能力。
