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A Comparative Study of Automatic Localization Algorithms for Spherical Markers within 3D MRI Data
Christian Fiedler1,2, Paul-Philipp Jacobs1, Marcel Müller3
1Department of Neurosurgery, University of Leipzig, 04103 Leipzig, SN, Germany.
Brain Sciences
|July 2, 2021
Summary
This study presents automated methods for detecting and localizing spherical features in 3D MRI scans, crucial for medical diagnostics and surgery planning. The research introduces novel algorithms and a marker design for precise spatial adjustments of medical imaging data.
Area of Science:
- Medical Image Processing
- Computer Vision
- Radiology
Background:
- Accurate localization of features in medical images is vital for diagnostics and surgical planning.
- Current methods often require human interaction and parameter tuning, highlighting the need for automated solutions.
Purpose of the Study:
- To develop and compare automated image processing pipelines for detecting and localizing spherical features in 3D MRI data.
- To introduce a novel spherical MRI marker design to enhance localization accuracy.
Main Methods:
- Four distinct image processing pipelines were developed and evaluated.
- Methods include convolution-based approaches, connected-components analysis, circular Hough transform, and Hessian determinant-based blob detection.
- A novel spherical MRI marker was designed for improved detection.
Main Results:
- The proposed algorithms and pipelines enable automatic detection and spatial localization of spherical features.
- The combination of novel marker design and algorithms allows for precise localization, including directional information, of fiducials and bone-anchors.
Conclusions:
- Automated detection and localization of spherical features in 3D MRI data are achievable with the proposed methods.
- The developed pipelines and novel marker design offer a fast, reliable, and automated solution for medical image analysis, aiding in diagnostics and surgery planning.
