Team VisAGeS

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Other Grants and Activities
Dissemination
Bibliography

Section: Scientific Foundations

Keywords : Rigid registration, deformable registration, similarity measures.

Registration

Image registration consists in finding a geometrical transformation in order to match n sets of images. Our objective is to work both, on rigid registration methods in order to develop new similarity measures for new imaging modalities, and on deformable registration to address the problem of tissue dissipation.

The registration between two images can be summarized by the expression  [39] :

Im1 $\mover {\#952 \#8712 \#920 ~}\mstyle {{arg~min}_\#936 ^{}\#916 \mfenced o=( c=) {\#934 _\#952 \mfenced o=( c=) \#937 _s-\#937 _t}}$

where $ \upper_omega$s and $ \upper_omega$t are respectively the two homologous sets of features respectively extracted from the source and the target images. These sets represent the two images in the registration process. They can be very different in nature, and can be deduced from a segmentation process (points, contours, crest lines ...) or directly from the image intensities (e.g. the joint histogram). Im2 $\#934 _\#952 $ is the transformation, ( $ \theta$ $ \in$$ \upper_theta$ being the set of parameters for this transformation), $ \upper_delta$ is the cost (or similarity) function, and $ \upper_psi$ is the optimization method. { $ \upper_omega$, $ \upper_phi$, $ \upper_delta$, $ \upper_psi$ } are the four major decisive factors in a registration procedure, the set $ \upper_theta$ being a priori defined. In addition to new evolutions of these factors, a constant concern is to propose a methodology for validating this registration procedure. We already have been largely involved in these aspects in the past and will maintain this effort  [44] , [48] .

In the domain of rigid registration, our research is more focused on new problems coming from the applications. For instance, the mono and multimodal registration of ultrasound images is still an open problem. In this context we are working in looking at new similarity measures to better take into account the nature of the echographic signal. Similarly, in the interventional theatre, new matching procedures are required between for instance video, optical or biological images and the pre-operative images (CT, MRI, SPECT/PET, Angiography ...). Some of these problems can be very challenging. For a number of new applications, there are no existing solutions to solve these problems (e.g. fusion of biological images with interventional images and images coming from the planning).

In many contexts, a rigid transformation cannot account for the underlying phenomena. This is for instance true when observing evolving biological and physiological phenomena. Therefore, deformable registrationmethods (also called non-rigid registration) are needed  [45] . In this domain, we are working in the following three directions:


previous
next

Logo Inria