Segmentation of in vivo target prior to tracking

Flexible endoscopes are used in many diagnostic and interventional procedures. Physiological motions may be very difficult to handle with such device, hence disturbing the physician in completing his task. One way of dealing with these motions is to have the endoscope following them on its own. To achieve such a goal one needs to motorize the flexible endoscope and to know accurately the position of the region of interest (target), in order to control the motors. To this purpose a tracking algorithm is used, which estimates the position of the target in the images acquired by the camera of the endoscope. But the tracking algorithm needs to be initialized correctly so as not to lose the target. The difficulty is that targets have many different characteristics and we have no prior knowledge about them. Besides we want the algorithm to be user friendly, particularly for the physicians, which means that no parameter has to be tuned even with completely different targets. The proposed algorithm computes a modified gradient image from first-order moments to obtain smooth edges and reduce the number of regions found during the next step. A watershed method is used to detect regions. Thanks to the previous processing of the image, most irrelevant regions will not be detected. Then a merging process is applied which results in a region corresponding to the target. From the border of this region we find a patch that will be used to initialize the tracking algorithm. Experimental results are promising.