Vercauteren, Tom (2008) Image Registration and Mosaicing for Dynamic In Vivo Fibered Confocal Microscopy. PhD thesis Informatique Temps Réel, Automatique et Robotique, CMA - Centre de Mathématiques Appliquées, ENSMP p.162.

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Abstract

Classical confocal microscopy can be used to obtain high-resolution images of cells in

tissue samples or cell cultures. Translation of this technology for in vivo applications

can be achieved by using optical fibers and miniature optics. Ultimately, fibered

confocal microscopy should enable clinicians and biologists to perform what can be

referred to as an optical biopsy: a real-time histological examination of biological

tissues in the living organism directly onto the region of interest.

The main goal of this thesis is to move beyond current hardware limitations of

these imaging devices by developing specific innovative image registration schemes.

In particular, this manuscript is framed by the goal of providing, through video

sequence mosaicing tools, wide field-of-view optical biopsies to the clinicians. This

targeted application is seen as a pipeline that takes raw data as input and provides

wide field-of-view image mosaics as output. We detail the critical building blocks of

this pipeline, namely real-time image reconstruction, linear image registration and

non-rigid registration, before presenting our mosaicing framework.

The raw data that fibered confocal microscopy produces is difficult to use as

it is modulated by a fiber optics bundle pattern and distorted by geometric arti-

facts. In this context, we show that real-time image reconstruction can be used

as a preprocessing step to get readily interpretable video sequences. Since fibered

confocal microscopy is a contact imaging modality, the combined movement of the

imaged tissues and the flexible optical microprobe makes it sometimes difficult to

get robust and accurate measurements of parameters of interest. To address this

problem, we investigated efficient and robust registration of pairs of images. We

show that registration tools recently developed in the field of vision-based robot

control can outperform classical image registration solutions used in biomedical im-

age analysis. The adequacy of these tools for linear image registration led us to

revisit non-rigid registration. By casting the non-rigid registration problem as an

optimization problem on a Lie group, we develop a fast non-parametric diffeomor-

phic image registration scheme. In addition to being diffeomorphic, our algorithm

provides results that are similar to the ones from Thirion’s demons algorithm but

with transformations that are smoother and closer to the true ones.

Finally, we use these image reconstruction and registration building blocks to

propose a robust mosaicing algorithm that is able to recover a globally consistent

alignment of the input frames, to compensate for the motion distortions and to

capture the non-rigid deformations. Moreover, our mosaicing algorithm has re-

cently been incorporated within a multicenter clinical trial. This trial illustrates

the clinical value of our tools in the particular application of Barrett’s esophagus

surveillance.

iii

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