An extension of digital volume correlation for multimodality image registration

Abstract

Abstract. The question of registering two images (or image volumes) acquired with different modalities, and thus exhibiting different contrast, at different positions is addressed based on an extension of global Digital Image (or Volume) Correlation. A specific comparison metric is introduced allowing the signature of the different phases to be related. A first solution consists of Gaussian mixture to describe the joint distribution of gray levels, which not only provides a matching of both images, but also offers a natural segmentation indicator. A second 'self-adapting' solution does not include any postulated a priori model for the joint histogram and leads to a registration of the images based on their initial histograms. The algorithm is implemented with a pyramidal multiscale framework for robustness. The proposed multiscale technique is tested on two 3D images obtained from X-ray and neutron tomography respectively. The proposed approach brings the two images to coincidence with a sub-pixel accuracy and allows for a "natural" segmentation of the different phases.

Digital Volume Correlation: Review of Progress and Challenges

“…It can therefore be adapted to many types of radiations as diverse as neutrons [224,225], muons, electrons or gamma radiations [24,173,172,174], visible optics, THz electromagnetic radiations, magnetic fields or ultrasound.…”

“…One such example was studied in details [225], where x-ray and neutron tomographic images of the same sandstone sample were captured, and a common referential was sought to fully benefit from both modalities and their resulting contrast. The presence of elements that display different contrasts with only one modality forbids the use of parametric relationships, yet it is possible to design a learning scheme that is a simple extension of standard DVC, and that leads to registration and simultaneously allows the different phases to be naturally segmented.…”

“…In Ref. [225], it was shown that the analytical formulation of the Newton's descent minimization algorithm assumes a form that is a natural extension of the incremental correction, see Equation (24). Moreover, if Φ 2 is adjusted from a previous determination of the registration, such an approach can be seen as a learning scheme of the suited potential to be minimized.…”

“…Such an approach was shown to be successful for x-neutron tomography registration that was performed for a Bentheim sandstone with a rather similar resolution [225]. …”

Dynamics of Water Absorption in Callovo-Oxfordian Claystone Revealed With Multimodal X-Ray and Neutron Tomography

“…To this end a "multimodal registration" tool is used as first presented in Tudisco et al (2017) and subsequently developed in Roubin et al (2019). The inherent linearisation of the problem requires a good initial guess of the solution for registration to converge.…”