van de Giessen Martijn, de Raedt Sepp, Stilling Maiken, Hansen Torben B, Maas Mario, Streekstra Geert J, van Vliet Lucas J, Vos Frans M
Quantititative Imaging Group, Delft University of Technology, The Netherlands.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):360-7. doi: 10.1007/978-3-642-23629-7_44.
The trapeziometacarpal joint enables the prehensile function of the thumb. Unfortunately, this joint is vulnerable to osteoarthritis (OA) that typically affects the local shape of the trapezium. A novel, local statistical shape model is defined that employs a differentiable locality measure based on the weighted variance of point coordinates per mode. The simplicity of the function and the smooth derivative enable to quickly determine localized components for densely sampled surfaces. The method is employed to assess a set of 60 trapezia (38 healthy, 22 with OA). The localized components predominantly model regions affected by OA, contrary to shape variations found with PCA. Furthermore, identification of pathological trapezia based on the localized modes of variation is improved compared to PCA.
大多角骨-掌骨关节实现了拇指的抓握功能。不幸的是,该关节易患骨关节炎(OA),这种疾病通常会影响大多角骨的局部形状。定义了一种新颖的局部统计形状模型,该模型基于每个模式下点坐标的加权方差采用可微的局部性度量。该函数的简单性和平滑导数能够快速确定密集采样表面的局部组件。该方法用于评估一组60个大多角骨(38个健康的,22个患有骨关节炎)。与主成分分析(PCA)发现的形状变化相反,局部组件主要对受骨关节炎影响的区域进行建模。此外,与主成分分析相比,基于局部变化模式对病理性大多角骨的识别得到了改进。