Fiducial Registrations

-Undergraduate Thesis- Updated April 10th,2022

Author: Matteo Tanzi

Year: 2022

Purpose: Undergraduate Thesis

Currently, algorithms for point-based, line-based and dynamic registrations are being developed to be ported into 3D Slicer as modules

Github Repository Structure

This github repository contains Jupyter Notebooks and Python Programs used for my undergrad thesis project

The Python Programs are found within the programs folder - descriptions can be found below

The Jupyter Notebooks - Found within Documentation - will outline the research and development techniques used for the point-set registration programs, using code blocks and text to improve clarity for reports for further research.

Project Description:

Understanding Fiducial Registration

Program Descriptions:

Fiducial Registration Technique applications

All sources for research and development are listed below:

• Orthogonal Procrustes Analysis (OPA)
• Line-based Orthogonal Procrustes Analysis
• Optimal solution in the presence of heteroscedastic Fiducial Localization Error
• ICP Registration
• ICP & Point Cloud Registration - Part 1: Known Data Association & SVD (Cyrill Stachniss, 2021)
• ICP & Point Cloud Registration - Part 2: Unknown Data Association (Cyrill Stachniss, 2021)
• Point-Set Registration
• Orthogonal Procrustes Analysis with isotropic scaling
• Point Registration
• Image Registration Techniques
• Orthogonal Procrustes problem
• Point Set Registration: Coherent Point Drift
• PyCPD: Tutorial on the Coherent Point Drift Algorithm
• Rigid Registration: The Iterative Closest Point Algorithm
• The Distribution of Target Registration Error in Rigid-Body Point-Based Registration
• Registration Errors, Terminology and Interpretation
• Registration and error estimation in correlated multimodal imaging
• Target Registration Error