A viterbi approach to topology inference for large scale endomicroscopy video mosaicing

Endomicroscopy allows in vivo and in situ imaging with cellular resolution. One limitation of endomicroscopy is the small field of view which can however be extended using mosaicing techniques. In this paper, we describe a methodological framework aiming to reconstruct a mosaic of endomicroscopic images acquired following a noisy robotized spiral trajectory. First, we infer the topology of the frames, that is the map of neighbors for every frame in the spiral. For this, we use a Viterbi algorithm considering every new acquired frame in the current branch of the spiral as an observation and the index of the best neighboring frame from the previous branch as the underlying state. Second, the estimated transformation between each spatial pair previously found is assessed. Mosaicing is performed based only on the pairs of frames for which the registration is considered successful. We tested our method on 3 spiral endomicroscopy videos each including more than 200 frames: a printed grid, an ex vivo tissue sample and an in vivo animal trial. Results were statistically significantly improved compared to reconstruction where only registration between successive frames was used.