  Predicting registration error can be useful for evaluation of registration
procedures, which is important for the adoption of registration techniques in
the clinic. In addition, quantitative error prediction can be helpful in
improving the registration quality. The task of predicting registration error
is demanding due to the lack of a ground truth in medical images. This paper
proposes a new automatic method to predict the registration error in a
quantitative manner, and is applied to chest CT scans. A random regression
forest is utilized to predict the registration error locally. The forest is
built with features related to the transformation model and features related to
the dissimilarity after registration. The forest is trained and tested using
manually annotated corresponding points between pairs of chest CT scans in two
experiments: SPREAD (trained and tested on SPREAD) and inter-database
(including three databases SPREAD, DIR-Lab-4DCT and DIR-Lab-COPDgene). The
results show that the mean absolute errors of regression are 1.07 $\pm$ 1.86
and 1.76 $\pm$ 2.59 mm for the SPREAD and inter-database experiment,
respectively. The overall accuracy of classification in three classes (correct,
poor and wrong registration) is 90.7% and 75.4%, for SPREAD and inter-database
respectively. The good performance of the proposed method enables important
applications such as automatic quality control in large-scale image analysis.
