ROI constrained statistical surface morphometry

This paper presents a novel ROI constrained statistical surface analysis framework that aims to accurately and efficiently localize regionally specific shape changes between groups of 3D surfaces. With unknown distribution of the data, existing shape morphometry analysis involves testing thousands of hypotheses for statistically significant effects through permutation. In this work, we develop a hybrid method to improve the system´s efficiency by computing the raw p-values of the nonparametric permutation tests only within a region of interest (ROI) of the surface. The ROI is identified through a parametric Pearson type III distribution approximation. Furthermore, a ROI based adaptive procedure is utilized to control the false discovery rate (FDR) for increased power of finding the significance.