A robust similarity measure for nonrigid image registration with outliers

Abstract

Nonrigid image registration is a widely used technique in medical imaging. While most methods work very well on images without pathologies or artefacts, there is a high need for improved robustness on images from pathological subjects and acquisitions with artefacts such as intensity inhomogeneity. In this paper, we propose a novel similarity measure based on normalised gradients for nonrigid registration, which is robust on images with intensity inhomogeneities or pathologies. We provide both theoretical and experimental proof of the robustness and evaluate the approach on manually segmented and simulated pathological images. Compared to normalised mutual information and to an alternative similarity also based on normalised gradients, we obtain significant overlap improvements for images with intensity inhomogeneities. We further confirm improved robustness on images with simulated tumours.

A robust similarity measure for volumetric image registration with outliers

“…In Ref. [24], we gave preliminary results that show that the cosine of normalised gradient orientations represents a robust similarity measure in the presence of both occlusions and intensity inhomogeneities. We extend this work in two major areas:…”