Image registration and mosaicing for dynamic In vivo fibered confocal microscopy

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 artifacts. 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 image 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 diffeomorphic 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 recently 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.