A surface-based metric for registration error quantification

Abstract

In medical image registration, the quantification of registration errors is important in deciding the capabilities of a registration technique for a given problem, and/or for a given pair of images. The most common approach is the geometrical registration error called Target Registration Error (TRE) that measures the distance between corresponding landmarks in the target and registered images. However, finding sufficient number of corresponding landmarks is not always possible in medical images, and therefore, other measures such as, image similarity measures and surface-based error metrics have been used in quantification of registration errors. Surface-based error quantification is more appropriate than intensity-based methods, but the widely used surface-based Closest Point Registration Error (CPRE) is known for under-estimating registration errors. In this paper, we present a surface-based method for quantification of registration errors using Matched Points Registration Error (MPRE) by computing distances between “matched-points” on segmented object surfaces in target and registered images. We compared small rigid registration errors of tube-shaped and closed surface objects quantified using MPRE with TRE and CPRE, and showed that MPRE did not show a significant difference from TRE and that CPRE was significantly lower than both MPRE and TRE.