Medical endoscopy is used in a wide variety of diagnostic and surgical procedures. These procedures are renowned for the difficulty of orienting the camera and instruments inside the human body cavities. The small size of the lens causes radial distortion of the image, which hinders the navigation process and leads to errors in depth perception and object morphology. This article presents a complete software-based system to calibrate and correct the radial distortion in clinical endoscopy in real time. Our system can be used with any type of medical endoscopic technology, including oblique-viewing endoscopes and HD image acquisition. The initial camera calibration is performed in an unsupervised manner from a single checkerboard pattern image. For oblique-viewing endoscopes the changes in calibration during operation are handled by a new adaptive camera projection model and an algorithm that infer the rotation of the probe lens using only image information. The workload is distributed across the CPU and GPU through an optimized CPU+GPU hybrid solution. This enables real-time performance, even for HD video inputs. The system is evaluated for different technical aspects, including accuracy of modeling and calibration, overall robustness, and runtime profile. The contributions are highly relevant for applications in computer-aided surgery and image-guided intervention such as improved visualization by image warping, 3-D modeling, and visual SLAM.

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Estimating the amount and center of distortion from lines in the scene has been addressed in the literature by the socalled "plumb-line" approach. In this paper we propose a new geometric method to estimate not only the distortion parameters but the entire camera calibration (up to an "angular" scale factor) using a minimum of 3 lines. We propose a new framework for the unsupervised simultaneous detection of natural image of lines and camera parameters estimation, enabling a robust calibration from a single image. Comparative experiments with existing automatic approaches for the distortion estimation and with ground truth data are presented.

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Este é um artigo de acesso aberto, licenciado por Creative Commons Atribuição 4.0 Internacional (CC BY 4.0), sendo permitidas reprodução, adaptação e distribuição desde que o autor e a fonte originais sejam creditados.

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