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Updated: Dec 23, 2025

Quantifying Intermembrane Distances with Serial Image Dilations
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Groupwise Image Alignment via Self Quotient Images.

Nefeli Lamprinou1, Nikolaos Nikolikos1, Emmanouil Z Psarakis1

  • 1Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece.

Sensors (Basel, Switzerland)
|April 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an improved groupwise image registration method using Self Quotient Images (SQI). It efficiently handles large, distorted, and multimodal image sets with low complexity, showing promising results in alignment quality.

Keywords:
congealigngroupwise registrationimage alignmentmedical imagingmultimodal alignmentpartially occluded image alignmentphotometrically distorted image alignmentself quotient image

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Image processing

Background:

  • Groupwise image registration enables unbiased, simultaneous analysis of multiple images.
  • Existing pixel-level Least-Squares (LS)-based methods have limitations with complex image variations.

Purpose of the Study:

  • To enhance state-of-the-art LS-based groupwise image registration.
  • To develop a method robust to photometric distortion, occlusion, and multimodal data.
  • To achieve linear complexity for large image set registration.

Main Methods:

  • Adaptation of LS-based groupwise registration using Self Quotient Images (SQI).
  • Development of a technique with linear complexity relative to the number of images.
  • Evaluation using mean Peak Signal to Noise Ratio (mPSNR) and mean Structural Similarity (mSSIM).

Main Results:

  • The proposed SQI-based method successfully registered images with photometric/geometric distortions and occlusion.
  • Effective registration was achieved for both unimodal and multimodal magnetic resonance image sets.
  • The method demonstrated promising alignment quality and computational efficiency compared to the Lucas-Kanade Entropy (LKE) algorithm.

Conclusions:

  • The enhanced groupwise registration technique effectively handles challenging image datasets.
  • The method offers a scalable and computationally efficient solution for large-scale image registration.
  • SQI-based groupwise registration presents a robust approach for diverse imaging applications.