Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 8, 2026

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

Automatic point correspondence using an artificial immune system optimization technique for medical image

Konstantinos K Delibasis1, Pantelis A Asvestas, George K Matsopoulos

  • 1Department of Computer Science and Biomedical Informatics, University of Central Greece, Greece.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|October 5, 2010
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Segmentation-Free Preoperative 3D MRI Classification of Low-Grade Versus High-Grade Glioma Using Task-Oriented Neural Architecture Search.

Journal of imaging·2026
Same author

Parametrization of microbial survival models under UVC exposure.

Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology·2026
Same author

EEG Connectivity Signatures in Active vs. Passive Mental Fatigue Settings.

IEEE journal of biomedical and health informatics·2026
Same author

PANTHER Challenge Report: Cross-Domain Pancreatic Tumor Segmentation in Magnetic Resonance Imaging.

Medical image analysis·2026
Same author

Beyond target lesions: Prognostic value of longitudinal AI-derived whole-body [<sup>1</sup>⁸F]FDG PET/CT metrics in metastatic melanoma.

European journal of nuclear medicine and molecular imaging·2026
Same author

MRIgRT real-time target tracking: TrackRAD2025 challenge report.

Medical image analysis·2026

An artificial immune system (AIS) method accurately finds corresponding points in medical images. This novel approach outperforms traditional Iterative Closest Point (ICP) and Mutual Information methods for image registration.

Area of Science:

  • Medical image analysis
  • Artificial intelligence in healthcare
  • Computational immunology

Background:

  • Accurate point correspondence is crucial for medical image registration.
  • Traditional methods like ICP and Mutual Information have limitations in accuracy and robustness.
  • Artificial Immune Systems (AIS) offer a novel, population-based optimization approach inspired by biological immunity.

Purpose of the Study:

  • To develop and evaluate an automatic method for determining corresponding points between medical images using an Artificial Immune System (AIS).
  • To compare the performance of the proposed AIS-based method against the Iterative Closest Point (ICP) algorithm and Mutual Information (MI) for image registration accuracy.

Main Methods:

  • Implementation of an Artificial Immune System (AIS) for function optimization.

More Related Videos

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jun 8, 2026

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

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

  • Application of AIS for extracting optimal candidate points and their correspondences in reference and secondary medical images.
  • Evaluation of point correspondence and registration accuracy using both AIS and ICP algorithms, alongside Mutual Information as a benchmark.

    • Main Results:

    • The proposed AIS algorithm demonstrated superior performance in identifying correct point correspondences compared to ICP.
    • AIS-based registration accuracy surpassed both ICP and Mutual Information methods.
    • Evaluated on 92 X-ray dental and 10 retinal image pairs with known and unknown transformations.

    Conclusions:

    • The AIS-based method provides a more accurate and robust approach for establishing point correspondences in medical images.
    • This novel application of AIS significantly improves medical image registration accuracy.
    • AIS presents a promising alternative to existing methods for complex image analysis tasks.