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: Jul 7, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Texture segmentation using filters with optimized energy separation.

T Randen, J H Husøy

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 12, 2008
    PubMed
    Summary
    This summary is machine-generated.

    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

    Optimal filter-based detection of microcalcifications.

    IEEE transactions on bio-medical engineering·2001
    Same author

    "Probability of successful defibrillation" as a monitor during CPR in out-of-hospital cardiac arrested patients.

    Resuscitation·2001
    Same author

    CPR artifact removal from human ECG using optimal multichannel filtering.

    IEEE transactions on bio-medical engineering·2000
    Same author

    Predicting outcome of defibrillation by spectral characterization and nonparametric classification of ventricular fibrillation in patients with out-of-hospital cardiac arrest.

    Circulation·2000
    Same author

    Optimisation algorithms for ECG data compression.

    Medical & biological engineering & computing·1997
    Same author

    Computationally efficient sub-band coding of ECG signals.

    Medical engineering & physics·1996

    New filter designs optimize texture feature extraction using novel energy separation criteria for improved segmentation. This research addresses both single and multiple texture challenges, enhancing image analysis capabilities.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Texture feature extraction is crucial for image analysis and segmentation.
    • Existing methods face challenges in separating features from complex textures.
    • A novel feature extraction model is proposed to address these limitations.

    Discussion:

    • The study introduces optimization approaches for filter design based on feature (energy) separation criteria.
    • These methods are developed to handle both two-texture and multiple-texture scenarios.
    • Supervised segmentation experiments are conducted to evaluate the effectiveness of the proposed approaches.

    Key Insights:

    • The developed optimization techniques demonstrate improved performance in texture segmentation.

    Related Experiment Videos

    Last Updated: Jul 7, 2026

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

  • The new feature extraction model provides a robust framework for analyzing textural information.
  • Comparative analysis includes results from alternative filter optimization strategies.

    • Outlook:

    • Further research can explore adaptive filter design for dynamic texture analysis.
    • The proposed model has potential applications in medical imaging and remote sensing.
    • Advancements in feature separation criteria could lead to more sophisticated texture descriptors.