A segmentation algorithm for a robotic micro-endoscope for exploration of the spinal cord

This work presents an adaptive segmentation algorithm for endoscopic images. It is part of a complete system for robot-assisted endoscopy of the human sub-arachnoid spinal space. The role of the vision system is to provide a feedback for assisting the navigation of the endoscope and helping avoid damages to delicate tissues. Due to the presence of small blood vessels, nerves, and possible fibrosis, a multi-step approach has been followed for segmentation of the lumen (corresponding to free space for navigation) and the other tissues. Histogram analysis, together with blob analysis and a modified implementation of the convex hull algorithm bring to the isolation of the lumen; by means of adaptive thresholding nerves are isolated; thresholding on the hue and saturation helps in recognizing the vessels. A special condition of dirty lumen helps in managing doubtful situations. Experimental trials have been conducted on video streams from endoscopic explorations of animal (pig) spinal cord in-vivo. Experimental results show that membranes, vessels, nerves, and lumen are recognized in a reliable way, so as to contribute to robot-assisted endoscopy. The speed of the processing resulted compatible with an envisaged use in real tune support of endoscopic navigation.