Regularized directional representations for medical image registration

TL;DR

This paper introduces a novel image registration method that aligns regularized vector fields derived from structural image features, improving accuracy across various modalities and anatomical regions.

Contribution

It proposes a new vector field similarity approach for mono- and multimodal registration that can be integrated with existing frameworks, enhancing registration performance.

Findings

Outperforms conventional methods on multiple datasets

Effective across diverse imaging modalities

Applicable to various anatomical locations

Abstract

In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion. Concurrently to these efforts, an increasing number of works have demonstrated that substantial gains in registration accuracy can also be achieved by aligning structural representations of images rather than images themselves. Following this research path, we propose a new method for mono- and multimodal image registration based on the alignment of regularized vector fields derived from structural information such as gradient vector flow fields, a technique we call \textit{vector field similarity}. Our approach can be combined in a straightforward fashion with any existing registration framework by substituting vector field similarity to intensity-based registration. In our experiments, we show that the proposed approach compares favourably with conventional image alignment on several public image datasets using a diversity of imaging modalities and anatomical locations.