1-point RANSAC for Circular Motion Estimation in Computed Tomography (CT)

TL;DR

This paper introduces a RANSAC-based method for accurately estimating the axial rotation angle in CT by using keypoints matching and outlier filtering, improving robustness over existing approaches.

Contribution

A novel RANSAC-based algorithm that estimates the rotation angle from a single correct correspondence in CT projections, enhancing accuracy and robustness.

Findings

The proposed method outperforms distribution-based approaches in accuracy.

It effectively filters out outliers in keypoints matching.

Experimental validation confirms improved rotation angle estimation.

Abstract

This paper proposes a RANSAC-based algorithm for determining the axial rotation angle of an object from a pair of its tomographic projections. An equation is derived for calculating the rotation angle using one correct keypoints correspondence of two tomographic projections. The proposed algorithm consists of the following steps: keypoints detection and matching, rotation angle estimation for each correspondence, outliers filtering with the RANSAC algorithm, finally, calculation of the desired angle by minimizing the re-projection error from the remaining correspondences. To validate the proposed method an experimental comparison against methods based on analysis of the distribution of the angles computed from all correspondences is conducted.