The medical diagnostic analysis and therapy of urinary bladder cancer based on endoscopes are state of the art in urological medicine. Due to the limited field of view of endoscopes, the physician can examine only a small part of the whole operating field at once. This constraint makes visual control and navigation difficult, especially in hollow organs. A panoramic image, covering a larger field of view, can overcome this difficulty. Directly motivated by a physician we developed an image mosaicing algorithm for endoscopic bladder fluorescence video sequences. In this paper, we present an approach which is capable of stitching single endoscopic video images to a combined panoramic image. Based on SIFT features we estimate a 2-D homography for each image pair, using an affine model and an iterative model-fitting algorithm. We then apply the stitching process and perform a mutual linear interpolation. Our panoramic image results show a correct stitching and lead to a better overview and understanding of the operation field.
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