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Related Concept Videos

Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...

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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Bootstrap resampling for image registration uncertainty estimation without ground truth.

Jan Kybic1

  • 1Center for Applied Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic. kybic@fel.cvut.cz

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 28, 2009
PubMed
Summary
This summary is machine-generated.

Estimating image registration uncertainty without ground truth is crucial. A novel bootstrap resampling method offers superior accuracy estimates compared to existing techniques like the Cramér-Rao bound.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Computational Science

Background:

  • Accurate image registration is vital for medical analysis.
  • Quantifying registration uncertainty is challenging without ground truth data.
  • Existing methods like Cramér-Rao bound have limitations.

Purpose of the Study:

  • To develop a novel, generalizable method for estimating pixel-based image registration uncertainty.
  • To evaluate the proposed method against state-of-the-art techniques.
  • To assess the performance of a fast registration accuracy estimation (FRAE) method.

Main Methods:

  • Utilized bootstrap resampling for uncertainty estimation.
  • Applied the method to various similarity criteria: SSD, SAD, correlation, and mutual information.
  • Experimentally compared bootstrap method with Cramér-Rao bound and FRAE.

Main Results:

  • The bootstrap method significantly improved registration accuracy estimation.
  • Bootstrap outperformed the Cramér-Rao bound method.
  • FRAE showed improvement over Cramér-Rao but was inferior to bootstrap.

Conclusions:

  • Bootstrap resampling provides a robust and generalizable approach for image registration uncertainty estimation.
  • The proposed method offers a significant advancement over current state-of-the-art techniques.
  • This work facilitates more reliable image registration in scenarios lacking ground truth.