Cancers located on the internal wall of bladders can be detected in image sequences acquired with endoscopes. The clinical diagnosis and follow-up can be facilitated by building a unique panoramic image of the bladder with the images acquired from different viewpoints. This process, called image mosaicing, consists of two steps. In the first step, consecutive images are pairwise registered to find the local transformation matrices linking geometrically consecutive images. In the second step, all images are placed in a common and global coordinate system. In this contribution, a mutual information-based similarity measure and a stochastic gradient optimization method were implemented in the registration process. However, the images have to be preprocessed in order to register the data in a robust way. Thus, a simple correction method of the distortions affecting endoscopic images is presented. After the placement of all images in the global coordinate system, the parameters of the local transformation matrices are all adjusted to improve the visual aspect of the panoramic images. Phantoms are used to evaluate the global mosaicing accuracy and the limits of the registration algorithm. The mean distances between ground truth positions in the mosaiced image range typically in 1-3 pixels. Results given for in vivo patient data illustrate the ability of the algorithm to give coherent panoramic images in the case of bladders.

Absfracl -This conlribution deals with the construction of a cartography of the internal surface of organs based on image sequences acquired during clinical video-tndoscopic exams. This paper describes a metrological approach to adapt the images acquisition conditions and the pre-processing tools to the evaluation of our specifically developed registration and mosaicing algorithm. A 3D-micrometric positioning system and an endoscope's tip holding structure was used to acquire sequences of images following a specific protocol. Forty images of a test scene (bladder planar photography) were acquired along a determined displacement looped path. An algorithm for correcting non-linear radial distortion caused by the endoscope was implemented. This algorithm computes projective (camera) and polynomial (distortion) transformations. The optimization process regislers the corrected distorted pattern image with the non-distorted one. Mutual information was used as a measure of similarity and stochastic gradient descent method for optimization. The visual quality of the images was then improved by a shading correction and by removing the optical fibers pattern from the images (specific low-pass filtering). A low-pass filter was used for obtaining the background that is subtracted from every image. The registration algorithm based on mutual inrormation as similarity measure and stochastic gradient descent as optimization method was used to obtain the transformation parameters that were applied to the images for building a mosaic (cartography). Finally, these parameters were applied to a similar sequence of images of a dots pattern (in place of the photography) allowing us to compute significant error parameters for the quality of the mosaicing. The results of the various processes are presented and the evaluation of the performancc of the cartography is discussed.

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