Intelligent visual localization of wireless capsule endoscopes enhanced by color information. (1st October 2017) - Dimas, George; Spyrou, Evaggelos; Iakovidis, Dimitris K.; Koulaouzidis, Anastasios
Abstract: Wireless capsule endoscopy (WCE) is performed with a miniature swallowable endoscope enabling the visualization of the whole gastrointestinal (GI) tract. One of the most challenging problems in WCE is the localization of the capsule endoscope (CE) within the GI lumen. Contemporary, radiation-free localization approaches are mainly based on the use of external sensors and transit time estimation techniques, with practically low localization accuracy. Latest advances for the solution of this problem include localization approaches based solely on visual information from the CE camera. In this paper we present a novel visual localization approach based on an intelligent, artificial neural network, architecture which implements a generic visual odometry (VO) framework capable of estimating the motion of the CE in physical units. Unlike the conventional, geometric, VO approaches, the proposed one is adaptive to the geometric model of the CE used; therefore, it does not require any prior knowledge about and its intrinsic parameters. Furthermore, it exploits color as a cue to increase localization accuracy and robustness. Experiments were performed using a robotic-assisted setup providing ground truth information about the actual location of the CE. The lowest average localization error achieved is 2.70 ± 1.62 cm, which is significantly lower than the error obtained with the geometric approach. This result constitutes a promising step towards the in-vivo application of VO, which will open new horizons for accurate local treatment, including drug infusion and surgical interventions. Highlights: A visual localization method for wireless capsule endoscopy is proposed. A multi-layer perceptron is used to solve the visual odometry problem. The use of color features enhances the visual localization performance. Validation is performed with a robotic-assisted experimental setup. Better localization performance than geometric visual odometry is reported.