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

Adapting pathology foundation models for continual cross-center WSI retrieval.

Medical image analysis·2026
Same author

A Multifunctional Hydrogel Integrating Hemostatic, Antioxidant, and Antibacterial Properties for Infected and Diabetic Wound Regeneration.

ACS applied materials & interfaces·2026
Same author

Gelatin-Based Multifunctional Hydrogels for Sports Injury Repair: Musculoskeletal and Nervous System Perspectives.

Gels (Basel, Switzerland)·2026
Same author

Integrating Pharmacovigilance Data Mining and Mendelian Randomization to Identify Risk Profiles and Causal Targets of Opioid-Induced Delirium.

CNS neuroscience & therapeutics·2026
Same author

Molecular Basis of Thermostability in a Novel Polygalacturonase <i>Ml</i>PG28A and Its Application in Producing Bioactive Oligogalacturonides for Sustainable Aquaculture.

Journal of agricultural and food chemistry·2026
Same author

Real-space imaging and control of topological spin textures in a van der Waals antiferromagnet.

Nature communications·2026

Related Experiment Video

Updated: Mar 31, 2026

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

8.1K

Application-Driven No-Reference Quality Assessment for Dermoscopy Images With Multiple Distortions.

Fengying Xie, Yanan Lu, Alan C Bovik

    IEEE Transactions on Bio-Medical Engineering
    |October 30, 2015
    PubMed
    Summary

    This study introduces an automated algorithm to assess dermoscopy image quality, crucial for accurate lesion analysis. The developed no-reference image quality assessment (IQA) model effectively predicts quality in images with blur and uneven illumination.

    More Related Videos

    Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
    07:50

    Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

    Published on: January 27, 2023

    3.9K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.9K

    Related Experiment Videos

    Last Updated: Mar 31, 2026

    Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
    07:03

    Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

    Published on: February 23, 2017

    8.1K
    Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
    07:50

    Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

    Published on: January 27, 2023

    3.9K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.9K

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Dermoscopy image acquisition can introduce blur and uneven illumination, negatively impacting automated lesion analysis.
    • Accurate image quality is essential for reliable diagnostic outcomes in dermatological applications.

    Purpose of the Study:

    • To develop an automated algorithm for assessing the quality of dermoscopy images.
    • To enable automatic identification of images requiring recapture or correction to improve diagnostic accuracy.

    Main Methods:

    • An application-driven, no-reference image quality assessment (IQA) model was developed for dermoscopy images.
    • A dataset of dermoscopy images with multiple distortions (blur, uneven illumination) was created.
    • A fuzzy neural network combined outputs from blur- and illumination-sensitive IQA metrics, using application-specific ground truth.

    Main Results:

    • The proposed model demonstrated accurate and stable quality prediction for dermoscopy images with multiple distortions.
    • Experimental results validated the model's effectiveness in assessing image quality affected by common acquisition artifacts.

    Conclusions:

    • The developed algorithm provides an effective solution for the quality assessment of multiply distorted dermoscopy images.
    • This work introduces an application-driven concept and a practical framework for image quality assessment in medical imaging.