Podsiadlo P, Wolski M, Stachowiak G W
Tribology Laboratory, School of Mechanical Engineering, The University of Western Australia, Crawley 6009, Western Australia.
Med Phys. 2008 May;35(5):1870-83. doi: 10.1118/1.2905025.
Osteoarthritic (OA) changes in knee joints can be assessed by analyzing the structure of trabecular bone (TB) in the tibia. This analysis is performed on TB regions selected manually by a human operator on x-ray images. Manual selection is time-consuming, tedious, and expensive. Even if a radiologist expert or highly trained person is available to select regions, high inter- and intraobserver variabilities are still possible. A fully automated image segmentation method was, therefore, developed to select the bone regions for numerical analyses of changes in bone structures. The newly developed method consists of image preprocessing, delineation of cortical bone plates (active shape model), and location of regions of interest (ROI). The method was trained on an independent set of 40 x-ray images. Automatically selected regions were compared to the "gold standard" that contains ROIs selected manually by a radiologist expert on 132 x-ray images. All images were acquired from subjects locked in a standardized standing position using a radiography rig. The size of each ROI is 12.8 x 12.8 mm. The automated method results showed a good agreement with the gold standard [similarity index (SI) = 0.83 (medial) and 0.81 (lateral) and the offset =[-1.78, 1.27]x[-0.65,0.26] mm (medial) and [-2.15, 1.59]x[-0.58, 0.52] mm (lateral)]. Bland and Altman plots were constructed for fractal signatures, and changes of fractal dimensions (FD) to region offsets calculated between the gold standard and automatically selected regions were calculated. The plots showed a random scatter and the 95% confidence intervals were (-0.006, 0.008) and (-0.001, 0.011). The changes of FDs to region offsets were less than 0.035. Previous studies showed that differences in FDs between non-OA and OA bone regions were greater than 0.05. ROIs were also selected by a second radiologist and then evaluated. Results indicated that the newly developed method could replace a human operator and produces bone regions with an accuracy that is sufficient for fractal analyses of bone texture.
膝关节的骨关节炎(OA)变化可通过分析胫骨小梁骨(TB)的结构来评估。这种分析是在操作人员在X射线图像上手动选择的TB区域上进行的。手动选择既耗时、繁琐又昂贵。即使有放射科专家或训练有素的人员来选择区域,观察者之间和观察者内部的高变异性仍然可能存在。因此,开发了一种全自动图像分割方法来选择骨区域,以便对骨结构变化进行数值分析。新开发的方法包括图像预处理、皮质骨板的描绘(主动形状模型)和感兴趣区域(ROI)的定位。该方法在一组独立的40张X射线图像上进行了训练。将自动选择的区域与“金标准”进行比较,“金标准”包含由放射科专家在132张X射线图像上手动选择的ROI。所有图像均使用射线照相设备从处于标准化站立姿势的受试者身上采集。每个ROI的大小为12.8×12.8毫米。自动化方法的结果与金标准显示出良好的一致性[相似性指数(SI)=0.83(内侧)和0.81(外侧),偏移量=[-1.78, 1.27]×[-0.65,0.26]毫米(内侧)和[-2.15, 1.59]×[-0.58, 0.52]毫米(外侧)]。针对分形特征构建了布兰德-奥特曼图,并计算了金标准与自动选择区域之间计算的分形维数(FD)相对于区域偏移量的变化。这些图显示出随机散点,95%置信区间为(-0.006, 0.008)和(-0.001, 0.011)。FD相对于区域偏移量的变化小于0.035。先前的研究表明,非OA和OA骨区域之间的FD差异大于0.05。第二位放射科医生也选择了ROI,然后进行评估。结果表明,新开发的方法可以取代人工操作人员,并产生足以进行骨纹理分形分析的精确骨区域。