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Stochastic optimization with randomized smoothing for image registration.

Wei Sun1, Dirk H J Poot2, Ihor Smal3

  • 1Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, USA.

Medical Image Analysis
|July 17, 2016
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Summary
This summary is machine-generated.

Randomized smoothing (RS) enhances image registration by smoothing the cost function with Gaussian noise during stochastic gradient descent (SGD) optimization, improving accuracy and robustness for various medical imaging datasets.

Keywords:
Image registrationLocal minimaRandomized smoothingStochastic gradient descent

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Area of Science:

  • Medical image analysis
  • Computational optimization
  • Computer vision

Background:

  • Image registration is a crucial process in medical imaging, often formulated as an optimization problem.
  • The presence of local minima in the cost function landscape can hinder accurate registration.
  • Smoothing the cost function is a desirable strategy to overcome local minima challenges.

Purpose of the Study:

  • To investigate the application of randomized smoothing (RS) for optimizing image registration.
  • To enhance the performance of stochastic gradient descent (SGD) by mitigating local minima issues.
  • To evaluate the effectiveness of RS in improving registration accuracy and robustness.

Main Methods:

  • Implementing a randomized smoothing (RS) technique within the stochastic gradient descent (SGD) optimization framework.
  • Introducing Gaussian noise to transformation parameters before computing the cost function gradient in each SGD iteration.
  • Applying the RS-SGD approach to both rigid and nonrigid image registration scenarios.

Main Results:

  • Demonstrated the effectiveness of the novel RS technique across diverse datasets, including synthetic, cell, CT lung, and MR brain images.
  • Showcased improvements in registration accuracy compared to standard optimization methods.
  • Highlighted the enhanced robustness of the RS approach in handling complex optimization landscapes.

Conclusions:

  • Randomized smoothing (RS) is a viable and effective technique for improving image registration.
  • The RS-SGD method successfully smooths the cost function, overcoming local minima and enhancing optimization.
  • The approach offers significant benefits for both rigid and nonrigid registration tasks in medical imaging.