Recently we developed analysis for 3D visceral organ deformation by combining the shape context (SC) method with a full-field strain (strain distribution on a whole 3D surface) analysis for calculating distension-induced rat stomach deformation. The surface deformation detected by the SC method needs to be further verified by using a feature tracking measurement. Hence, the aim of this study was to verify the SC method-based calculation by using digital image correlation (DIC) measurement on a rat stomach. The rat stomach exposed to distension pressures 0.0, 0.2, 0.4, and 0.6？kPa were studied using both 3D DIC system and SC-based image registration calculation. Three different surface sample counts between the reference and the target surfaces were used to gauge the effect of the surface sample counts on the calculation. Each pair of the surface points between the DIC measured target surface and the SC calculated correspondence surface was compared. Compared with DIC measurement, the SC calculated surface had errors from 5% to 23% at pressures from 0.2 to 0.6？kPa with different surface sample counts between the reference surface and the target surface. This indicates good qualitative and quantitative agreement on the surfaces with small dissimilarity and small sample count difference between the reference surface and the target surface. In conclusion, this is the first study to validate the 3D SC-based image registration method by using unique tracking features measurement. The developed method can be used in the future for analysing scientific and clinical data of visceral organ geometry and biomechanical properties in health and disease. 1. Introduction Identifying corresponding points between two configurations is a common problem in medical image processing. Point matching-based surface registration using 3D shape context (SC) has recently emerged as an alternative to intensity-based nonlinear registration. The major contribution of SC lies in the global shape characterization at a local level for each single point. The point matching is based on 3D shape descriptors that characterize the shape of each point based on histograms of the distribution of points around them. Corresponding points on similar shapes will have similar shape contexts. In comparison to other point matching techniques, SC do not require an equal number of points for the shapes to be compared or segments with specific geometrical configurations [1, 2]. Studies on lung and cortical surfaces demonstrated that the SC matching approach-based nonlinear registration method is a
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