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 …

This paper reports a new automatic algorithm to estimate the misregistration in a quantitative
manner. A random regression forest is constructed, predicting the local registration error …

Accuracy validation is essential to clinical application of medical image registration
techniques. Registration validation remains a challenging problem in practice mainly due to …

Medical image registration is a crucial task in many different medical imaging applications.
Hence, considerable amount of work has been published recently that aim to predict the …

A novel method for automatic quality assessment of medical image registration is presented.
The method is based on supervised learning of local alignment patterns, which are captured …

Given that image registration is a fundamental and ubiquitous task in both clinical and
research domains of the medical field, errors in registration can have serious consequences …

We introduce a boosting algorithm to improve on existing methods for deformable image
registration (DIR). The proposed DIRBoost algorithm is inspired by the theory on hypothesis …

Current registration evaluations typically compute target registration error using manually
annotated datasets. As a result, the quality of landmark annotations is crucial for unbiased …

Purpose: To present a new approach to the problem of estimating errors in deformable
image registration (DIR) applied to sequential phases of a 4DCT data set. Methods: A set of …

Error estimation in nonlinear medical image registration is a nontrivial problem that is
important for validation of registration methods. We propose a supervised method for …