Objective: In this paper, we present an autonomous virtual mobile robot (AVMR) for three-dimensional (3D) exploration of unknown tubular-like structures in 3D images.
Methods and materials: The trajectory planning for 3D central navigation is achieved by combining two neuro-fuzzy controllers, and is based on 3D sensory information; a Hough transform is used to locally fit a cylinder during the exploration, estimating the local radius of the tube. Nonholonomic constraints are applied to assure a smooth, continuous and unique final path. When applied to 3D medical images, the AVMR operates as a virtual endoscope, directly providing anatomical measurements of the organ. After a thorough validation on challenging synthetic environments, we applied our method to eight micro-CT datasets of cochleae.
Results: Validation on synthetic environments proved the robustness of our method, and highlighted key parameters for the design of the AVMR. When applied to the micro-CT datasets, the AVMR automatically estimated length and radius of the cochleae: results were compared to manual delineations, proving the accuracy of our approach.
Conclusions: The AVMR presents several advantages when used as a virtual endoscope: the nonholonomic constraint guarantees a unique and smooth central path, which can be reliably used both for qualitative and quantitative investigation of 3D medical datasets. Results on the micro-CT cochleae are a significant step towards the validation of more clinical computed tomography (CT) studies.