Predicting medical image registration error through independent directions

Abstract

Estimating registration error through independent directions, horizontal, vertical, and diagonal, is yet an unaccomplished task. If accurately done, this information can be used as feedback to further improve the registration quality. In this paper, we propose an algorithm for this purpose using a random forest regressor with features extracted using block matching. The proposed algorithm only requires two images as input and make predictions densely without requiring multiple registrations. The results on publicly available datasets show that the displacement of the best match after block matching provides strong cues about the registration error and the proposed algorithm is capable of estimating registration error through independent directions with high accuracy.