Nonlinear Distortion Correction in Endoscopic Video Images

Modern video-based endoscopes offer physicians a wide-angle field of view for minimally-invasive procedures. Unfortunately, inherent barrel distortion prevents accurate perception of range. This makes measurement and distance judgment difficult and causes difficulties in emerging applications, such as 3D medical-image registration. Such distortion also arises in other wide field-of-view camera circumstances. This paper presents a distortion-correction technique that can automatically calculate correction parameters, without precise knowledge of horizontal and vertical orientation. The method is applicable to any camera-distortion correction situation. Based on a least-squares estimation, our proposed algorithm considers line fits in both field-of-view directions and global consistency that gives the optimal image center and expansion coefficients. The method is insensitive to the initial orientation of the endoscope and provides more exhaustive field-of-view correction than previously proposed algorithms. The distortion-correction procedure is demonstrated for endoscopic video images of a calibration test pattern, a rubber bronchial training device, and real human circumstances. The distortion correction is also shown as a necessary component of an image-guided virtual-endoscopy system that matches endoscope images to corresponding rendered 3D CT views.