Graph based construction of textured large field of view mosaics for bladder cancer diagnosis

Large field-of-view panoramic images greatly facilitate bladder cancer diagnosis and follow-up. Such 2D mosaics can be obtained by registering the images of a video-sequence acquired during cystoscopic examinations. The scientific challenge in the registration process lies in the strong inter- and intra-patient texture variability of the images, from which primitives cannot be robustly extracted. State-of-the-art registration methods are not at the same time robust and accurate, especially for image pairs with a small amount of overlap (less than 90%) or strong perspective transformations. Moreover, no previous contribution to cystoscopy mosaicing presents panoramic images created from multiple overlapping sequences (e.g. "zigzags" or loop trajectories). We show how such overlapping sections can be automatically detected and present a novel registration algorithm that robustly superimposes non-consecutive image pairs, which are related by stronger perspective transformations and share less overlap than consecutive images (less than 50%). Globally coherent panoramic images are constructed using a non-linear optimization and a novel contrast-enhancing stitching method. Results on both phantom and patient data are obtained using constant algorithm parameters, which demonstrate the robustness of the proposed method. While the methods presented in this contribution are specifically designed for cystoscopy mosaicing, they can also be applied to more general mosaicing problems. We demonstrate this on a traditional stitching application, where a set of pictures of a building are stitched into a seamless, globally coherent panoramic image.