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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Apr 8, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Towards measuring neuroimage misalignment.

Revanth Reddy Garlapati¹, Ahmed Mostayed¹, Grand Roman Joldes¹

¹Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia.

Computers in Biology and Medicine

|June 27, 2015

Summary

This summary is machine-generated.

Evaluating image registration for neuro-navigation is crucial. Hausdorff Distance (HD) metrics like EBHD and RHD show promise for assessing alignment errors, unlike intensity-based metrics. Presenting HD as percentile-HD curves offers a comprehensive view of misalignments.

Keywords:

Brain deformation Hausdorff Distance Image similarity metrics Intra-operative registration Non-rigid registration

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Last Updated: Apr 8, 2026

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Area of Science:

Medical Imaging
Neurosurgery
Image Registration

Background:

Accurate pre-operative to intra-operative image registration is vital for neuro-navigation.
Evaluating alignment accuracy is critical to ensure patient safety and surgical precision.

Purpose of the Study:

To evaluate the effectiveness of different metrics in assessing image registration accuracy for neuro-navigation.
To compare Hausdorff Distance (HD)-based metrics with intensity-based similarity metrics.

Main Methods:

Two HD-based metrics: edge-based HD (EBHD) and Robust HD (RHD).
Intensity-based similarity metrics: Mutual Information (MI), Normalised Mutual Information (NMI), Entropy Correlation Coefficient (ECC), Kullback-Leibler Distance (KLD), and Correlation Ratio (CR).
Evaluation using known deformations on sample images and real brain shift cases.

Main Results:

Intensity-based metrics (MI, NMI, ECC, KLD, CR) showed poor correlation with actual alignment errors.
HD-based metrics (EBHD, RHD) correlated well with alignment errors but tended to underestimate them.
Percentile-HD curves provide a more comprehensive assessment of alignment errors than single-value metrics.

Conclusions:

Intensity-based metrics are not suitable for evaluating neuro-navigation image registration accuracy.
EBHD and RHD metrics are more reliable but require careful interpretation due to underestimation.
Percentile-HD curves are recommended for a thorough evaluation of image registration alignment errors.