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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Position Paper: Artificial Intelligence in Medical Image Analysis: Advances, Clinical Translation, and Emerging Frontiers.

IEEE journal of biomedical and health informatics·2025
Same author

Helios Observations of Quasiperiodic Density Structures in the Slow Solar Wind at 0.3, 0.4, and 0.6 AU.

Journal of geophysical research. Space physics·2020
Same author

Assessing the Quality of Models of the Ambient Solar Wind.

Space weather : the international journal of research & applications·2020
Same author

Adaptive emergency scenery video communications using HEVC for responsive decision support in disaster incidents.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016
Same author

Computer-aided diagnosis in hysteroscopic imaging.

IEEE journal of biomedical and health informatics·2014
Same author

Despeckle Filtering for Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Ultrasound Images of the Intima-Media Complex.

International journal of biomedical imaging·2014
Same journal

SinColor: Uncertainty-Guided Single-Step Diffusion for Image Colorization.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Jan 6, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification.

V Jatla, M S Pattichis, C N Arge

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 4, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study developed image processing methods to select physical models for predicting geomagnetic storms using solar coronal hole data. The validated approach achieved 95.5% accuracy in classifying solar maps.

    More Related Videos

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K
    A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
    10:39

    A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

    Published on: May 24, 2022

    2.7K

    Related Experiment Videos

    Last Updated: Jan 6, 2026

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    43.4K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K
    A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
    10:39

    A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

    Published on: May 24, 2022

    2.7K

    Area of Science:

    • * Solar Physics
    • * Space Weather Prediction
    • * Image Processing

    Background:

    • * Accurate physical models are crucial for predicting geomagnetic storms.
    • * Identifying and analyzing coronal holes in solar images is key to model selection.
    • * Existing methods for coronal hole segmentation and matching require improvement.

    Purpose of the Study:

    • * To develop and validate novel image processing techniques for selecting physical models of the solar corona.
    • * To improve the accuracy of coronal hole segmentation and matching for enhanced geomagnetic storm prediction.
    • * To establish a robust framework for classifying solar physical maps based on coronal hole features.

    Main Methods:

    • * Developed a multi-modal segmentation method combining three techniques to initialize a level-set method for precise coronal hole boundary detection.
    • * Introduced a Linear Programming approach for matching coronal hole clusters across different solar maps.
    • * Employed Random Forests for the final matching of coronal hole clusters.
    • * Validated methods using consensus maps, manual clustering, manual classification, and testing on 50 maps.

    Main Results:

    • * The multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCN in boundary detection accuracy.
    • * Achieved a 95.5% accuracy in classifying solar physical maps.
    • * Demonstrated effective matching of coronal hole clusters using Linear Programming and Random Forests.

    Conclusions:

    • * The developed image processing methods provide a significant advancement in selecting physical models for geomagnetic storm prediction.
    • * The multi-modal segmentation and cluster matching techniques offer high accuracy and reliability.
    • * This work lays the foundation for more precise space weather forecasting through improved analysis of solar imagery.