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: May 28, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

A novel computer-aided lung nodule detection system for CT images.

Maxine Tan1, Rudi Deklerck, Bart Jansen

  • 1Department of Electronics and Informatics , Vrije Universiteit Brussel, Brussel, Belgium.

Medical Physics
|October 14, 2011
PubMed
Summary

This study introduces a computer-aided detection (CAD) system for lung nodules in CT scans, utilizing a novel feature selection and classification method. The system demonstrates competitive performance against established classifiers, offering flexibility in analyzing complex medical images.

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

Do deep learning models learn beyond average gait? A comparison of architectures, kinematic representations, and marker sparsity for vertical ground reaction force prediction.

Gait & posture·2026
Same author

Construct validation of the vitality capacity domains 'energy and metabolism' and 'neuromuscular function' in relation to locomotor capacity and quality of life in community-dwelling middle-aged and older adults.

GeroScience·2026
Same author

Dynamics of brain-muscle interaction with neuromuscular fatigability: systematic review.

Frontiers in physiology·2026
Same author

Stress-related fluctuations in personality functioning in daily life: Pilot data from an ambulatory monitoring study in outpatients diagnosed with borderline personality disorder.

Clinical psychology & psychotherapy·2026
Same author

Releasing open-weight AI in steps would alleviate risks.

Nature·2026
Same author

Fractured Full-Arch Tooth-Supported Zirconia Bridge: Thin Design, Surface Damage, and Excessive Cement Layer Thickness.

Reports (MDPI)·2026

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Lung nodule detection in computed tomography (CT) is crucial for early cancer diagnosis.
  • Accurate differentiation between nodules and anatomical structures like blood vessels is challenging.
  • Existing computer-aided detection (CAD) systems require careful parameter tuning.

Purpose of the Study:

  • To present a complete CAD system for lung nodule detection in CT images.
  • To introduce and evaluate a novel mixed feature selection and classification methodology.
  • To compare the performance of this new methodology against established classifiers.

Main Methods:

  • The CAD system was developed using the Lung Image Database Consortium (LIDC) dataset.
  • A nodule segmentation method employed enhancement filters and a computed divergence feature.

More Related Videos

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
07:53

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

Related Experiment Videos

Last Updated: May 28, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
07:53

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

  • A novel feature-selective classifier using genetic algorithms and artificial neural networks (ANNs) was developed and compared with Support Vector Machines (SVMs) and fixed-topology ANNs.

    • Main Results:

    • A fixed-topology ANN classifier achieved 87.5% sensitivity for nodules >= 3mm with 4 false positives per scan.
    • The novel feature-selective classifier offers flexibility and adaptability without needing prior assumptions on node numbers.
    • False positives included a significant proportion (18%) of smaller nodules (< 3mm).

    Conclusions:

    • A comprehensive CAD system with novel features and comparative classifier analysis was presented.
    • The CAD system demonstrated robust performance across different classifiers, comparable to existing literature methods.
    • The feature-selective classifier provides an adaptable solution for complex lung nodule detection tasks.