Cystoscopy is used as a reference clinical examination in the detection and visualization of pathological bladder lesions. Evolution observation and analysis of these lesions is easier when panoramic images from internal bladder walls are used instead of video sequences. This work describes a fast and automatic mosaicing algorithm applied to cystoscopic video sequences, where perspective geometric transformations link successive image pairs. This mosaicing algorithm begins with a fast initialization of translation parameters computed by a cross-correlation of images, followed by an iterative optimization of transformation parameters. Finally, registered images are projected onto a global common coordinate system. A quantifying test protocol applied over a phantom yielded a mosaicing mean error lower than 4 pixels for a 1947 × 1187 pixels panoramic image. Qualitative evaluation of 10 panoramic images resulting from videos of clinical cystoscopies was performed. An analysis performed over translation values from these clinical sequences (in vivo) is used to modify the mosaicing algorithm to be able to do a dynamic selection of image pairs. Construction time of panoramic images takes some minutes. At last, algorithm limits are discussed.