Toward an Automatic Calibration of Dual Fluoroscopy Imaging Systems

Abstract. High-speed dual fluoroscopy (DF) imaging provides a novel, in-vivo solution to quantify the six-degree-of-freedom skeletal kinematics of humans and animals with sub-millimetre accuracy and high temporal resolution. A rigorous geometric calibration of DF system parameters is essential to ensure precise bony rotation and translation measurements. One way to achieve the system calibration is by performing a bundle adjustment with self-calibration. A first-time bundle adjustment-based system calibration was recently achieved. The system calibration through the bundle adjustment has been shown to be robust, precise, and straightforward. Nevertheless, due to the inherent absence of colour/semantic information in DF images, a significant amount of user input is needed to prepare the image observations for the bundle adjustment. This paper introduces a semi-automated methodology to minimise the amount of user input required to process calibration images and henceforth to facilitate the calibration task. The methodology is optimized for processing images acquired over a custom-made calibration frame with radio-opaque spherical targets. Canny edge detection is used to find distinct structural components of the calibration images. Edge-linking is applied to cluster the edge pixels into unique groups. Principal components analysis is utilized to automatically detect the calibration targets from the groups and to filter out possible outliers. Ellipse fitting is utilized to achieve the spatial measurements as well as to perform quality analysis over the detected targets. Single photo resection is used together with a template matching procedure to establish the image-to-object point correspondence and to simplify target identification. The proposed methodology provided 56,254 identified-targets from 411 images that were used to run a second-time bundle adjustment-based DF system calibration. Compared to a previous fully manual procedure, the proposed methodology has significantly reduced the amount of user input needed for processing the calibration images. In addition, the bundle adjustment calibration has reported a 50% improvement in terms of image observation residuals.