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 Videos

Informative frame classification for endoscopy video.

JungHwan Oh1, Sae Hwang, JeongKyu Lee

  • 1Department of Computer Science and Engineering, University of North Texas, P.O. Box 311366, NTRP F274, Denton, TX 76203, USA. jhoh@cse.unt.edu

Medical Image Analysis
|March 3, 2007
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

Biomaterial Strategies for Three-Dimensional Bioprinting and Drug Delivery Application.

Materials (Basel, Switzerland)·2026
Same author

A Novel Approach Combining Ultrasound-Induced Drug-Free Vasodilation and Photoacoustic Monitoring of Vascular Responses.

Annals of biomedical engineering·2026
Same author

MXene-based hybrid nanocomposites for enhanced enzyme-electrode electron transfer for high-performance glucose biosensing.

Bioelectrochemistry (Amsterdam, Netherlands)·2026
Same author

Decoding chemical composition of urinary crystals from ultrasonic echo signals via deep learning.

Mikrochimica acta·2026
Same author

Deep Learning-Based Super-Resolution for Vessel Enhancement in Photoacoustic Microscopy Imaging.

Journal of biophotonics·2026
Same author

AI-Based Electromyographic Analysis of Single-Leg Landing for Injury Risk Prediction in Taekwondo Athletes.

Healthcare (Basel, Switzerland)·2026
Same journal

HiVLR: Hierarchical Vision-Language Reasoning for interpretable zero-shot radiography image understanding.

Medical image analysis·2026
Same journal

FAA-Net: Fetal abdominal anomaly diagnosis in prenatal ultrasound via LLM-enhanced multi-instance learning.

Medical image analysis·2026
Same journal

Wavelet-inspired diffusion model with near-field constraint for real-time echocardiography dehazing.

Medical image analysis·2026
Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026
Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026
Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026
See all related articles

This study introduces new methods to identify clear, in-focus frames in endoscopy videos, improving computer-aided diagnosis by filtering out blurry, non-informative images.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Endoscopy Technology

Background:

  • Endoscopy enables minimally invasive internal body examination but often produces out-of-focus frames due to fixed-focus lenses.
  • These non-informative frames hinder real-time physician analysis and computer-aided diagnosis (CAD) systems.
  • Accurate classification of informative (in-focus) and non-informative (out-of-focus) frames is crucial for efficient data utilization.

Purpose of the Study:

  • To develop and evaluate novel techniques for classifying endoscopy video frames as informative or non-informative.
  • To enhance the accuracy of frame classification by addressing the challenge of specular reflections.
  • To improve the efficiency and effectiveness of computer-aided diagnosis in endoscopic procedures.

Main Methods:

Related Experiment Videos

  • Proposed two novel frame classification techniques: edge-based and clustering-based.
  • Developed a specular reflection detection technique to mitigate its impact on classification accuracy.
  • Integrated specular reflection detection with frame classification methods to boost overall performance.

Main Results:

  • Both edge-based and clustering-based methods achieved high accuracy in classifying informative and non-informative frames.
  • The specular reflection detection technique demonstrated precision, sensitivity, and specificity exceeding 90%.
  • The combined approach resulted in classification accuracy greater than 95% for informative frames.

Conclusions:

  • The proposed edge-based and clustering-based techniques effectively distinguish informative from non-informative endoscopy frames.
  • Specular reflection detection significantly improves the accuracy of informative frame classification.
  • These methods offer a valuable tool for enhancing computer-aided diagnosis and reducing physician workload in endoscopy.