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

MRI acute/sub-acute ischemic stroke segmentation with deep learning: A comprehensive review.

International review of cell and molecular biology·2026
Same author

Synergistic fusion of a multilevel visual transformer in CNN for variable-length volumetric radiographic data analysis and content-based retrieval.

Scientific reports·2026
Same author

A Benchmark Dataset for Concealed Improvised Explosive Device Detection in X-ray Security Imaging.

Scientific data·2026
Same author

Multi-representation thermal features for enhanced defect analysis in pulse thermography.

Scientific reports·2026
Same author

HBID24K: A New Benchmark Dataset for Vulnerable Houbara Bustard and Intruder Detection in Wildlife Monitoring.

Scientific data·2026
Same author

DUCore: Dual Uncertainty-Guided Consistency and Regional Contrastive Learning for Semi-supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics·2026

Related Experiment Video

Updated: Mar 27, 2026

Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples
04:57

Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples

Published on: April 25, 2025

1.2K

Automatic polyp detection: A comparative study.

Alaa El Khatib, Naoufel Werghi, Hussain Al-Ahmad

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study compares image descriptors for automatic polyp detection in colonoscopy videos. Histogram of Oriented Gradients (HOG) and Gabor filters showed the best performance for identifying polyps.

    More Related Videos

    Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
    08:56

    Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

    Published on: January 13, 2023

    2.9K
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    2.8K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples
    04:57

    Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples

    Published on: April 25, 2025

    1.2K
    Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
    08:56

    Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

    Published on: January 13, 2023

    2.9K
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    2.8K

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Gastroenterology

    Background:

    • Colorectal cancer (CRC) is a significant health concern.
    • Early polyp detection during colonoscopy is crucial for CRC prevention.
    • Automated polyp detection systems can aid endoscopists.

    Purpose of the Study:

    • To evaluate and compare the performance of various state-of-the-art image descriptors for polyp detection in colonoscopy videos.
    • To identify the most effective image descriptor and classification method for this task.

    Main Methods:

    • Utilized Local Binary Patterns (LBP), 2D Gabor filters, wavelet-based texture, and Histogram of Oriented Gradients (HOG) as image descriptors.
    • Employed Maximally Stable Extremal Regions (MSER) for candidate region selection.
    • Classified candidate regions using Support Vector Machine (SVM) and Nearest Neighbor (NN) classifiers.

    Main Results:

    • Performance scores were evaluated on the ASU-Mayo Clinic polyp database.
    • Specific descriptors and classifier combinations demonstrated varying degrees of effectiveness.
    • HOG and Gabor filters, when combined with appropriate classifiers, showed promising results for polyp detection.

    Conclusions:

    • The choice of image descriptor significantly impacts the performance of automatic polyp detection systems.
    • HOG and Gabor filters represent effective features for polyp identification in colonoscopy.
    • Further research can optimize these methods for clinical application.