Locally Normalized Gradient Fields for Multi-Modal Groupwise Image Registration

Abstract

The Normalized Gradient Fields (NGF) similarity measure has shown its dominance for registering pairs of images from numerous modalities. Recently, gradient fields-based distance measures for groupwise image registration have been proposed, but these works have shown its usefulness only for mono-modal applications or dynamic imaging from the same modality. In this paper, we propose the groupwise Normalized Gradient Fields (gNGF) similarity measure for groupwise multi-modal image registration. The normalization of gradient fields is one of the key components of the NGF measure where the normalization may be computed locally or globally. In this work, we motivate the local normalization for multi-modal registration where the correlations between gradient fields are maximized at every point in the spatial domain for registering images. We demonstrate the effectiveness and applicability to real-world data of our proposal through experiments on images from two datasets. The results show that gNGF may be a long missing extension of NGF for multi-modal, multi-image applications.

Tasks

Reproductions