Novel RANSAC-based method for detecting and estimating externally attached marker spheres in craniomaxillofacial CT images.

ABSTRACT:

Background

The precision of image-physical space registration using spherical markers in craniomaxillofacial surgical navigation significantly depends on the accurate estimation of spherical parameters from computed tomography (CT) images. However, this estimation is susceptible to the abnormal points caused by artifacts, instruments interference, and other factors. To address these challenges, this study proposes a robust method to improve reproducibility in results and achieve higher accuracy on low inlier ratio data, thereby meeting the requirements of high-precision surgical applications.

Methods

Firstly, potential marker sphere regions are isolated from CT images. Next, we propose the Local Evaluation and Optimization RANdom SAmple Consensus (LEO-RANSAC) algorithm to re

SUBMITTER: Li Y 

PROVIDER: S-EPMC12397674 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature