Homography-based robust pose compensation and fusion imaging for augmented reality based endoscopic navigation system. (November 2021)

• Record Type: Journal Article
• Authors: Li, Wenjie; Fan, Jingfan; Li, Shaowen; Tian, Zhaorui; Ai, Danni; Song, Hong; Yang, Jian
• Is Part Of: Computers in biology and medicine. Volume 138(2021)
• Publication Date: 2021-11
• Subjects: Augmented reality -- Online calibration -- Endoscope calibration -- Camera pose compensation -- Image-to-patient registration; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104864
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Augmented reality (AR) based fusion imaging in endoscopic surgeries rely on the quality of image-to-patient registration and camera calibration, and these two offline steps are usually performed independently to get the target transformation separately. The optimal solution can be obtained under independent conditions but may not be globally optimal. All residual errors will be accumulated and eventually lead to inaccurate AR fusion. Methods: After a careful analysis of the principle of AR imaging, a robust online calibration framework was proposed for an endoscopic camera to enable accurate AR fusion. A 2D checkerboard-based homography estimation algorithm was proposed to estimate the local pose of the endoscopic camera, and the least square method was used to calculate the compensation matrix in combination with the optical tracking system. Results: In comparison with conventional methods, the proposed compensation method improved the performance of AR fusion, which reduced physical error by up to 82%, reduced pixel error by up to 83%, and improved target coverage by up to 6%. Experimental results of simulating mechanical noise revealed that the proposed compensation method effectively corrected the fusion errors caused by the rotation of the endoscopic tube without recalibrating the camera. Furthermore, the simulation results revealed the robustness of the proposed compensation method to noises. Conclusions: Overall, the experiment results proved the effectiveness of the proposed compensation method and online calibration framework, and revealed a considerable potential in clinical practice. Highlights: An online calibration framework led to accurate augmented reality fusion. Compensation of residual error in two-step calibration improves the fusion quality. The compensation method improves the robustness of fusion result to noises. Fusion errors caused by camera rotation can be corrected by online calibration.