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

Ready or Not? Program Directors' Preparedness for the General Pediatrics Enstrustable Professional Activity Framework.

Academic pediatrics·2026
Same author

Development and Validation of a Pathomics Model for Prognosis Prediction in Neoadjuvant Therapy-Treated Breast Cancer: A Retrospective, Multicenter Study.

MedComm·2026
Same author

Cognitive reserve proxies predict cognition and motor function beyond multimodal MRI brain measures in healthy adults.

Biological psychology·2026
Same author

Digital decoding tissue microenvironment heterogeneity from spatial proteomics through graph-enhanced transfer learning.

Cell systems·2026
Same author

Defining Practice Ready: Ensuring Training and Certification Are Designed to Meet Patient Needs.

Pediatrics·2026
Same author

Oxygen-Vacancy-Engineered Y<sub>2</sub>O<sub>3</sub>/CeO<sub>2</sub> Nanobrush Superlattices via Laser Heteroepitaxy: Toward High-Performance Memristors.

Small methods·2026

Related Experiment Video

Updated: Jun 4, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Computer-aided diagnosis of pulmonary infections using texture analysis and support vector machine classification.

Jianhua Yao1, Andrew Dwyer, Ronald M Summers

  • 1Center for Infectious Disease Imaging (CIDI) and Department of Radiology and Image Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.

Academic Radiology
|February 8, 2011
PubMed
Summary

Computer-assisted detection using texture analysis and support vector machine classification can accurately identify and quantify pulmonary abnormalities on chest CT scans for infections like H1N1 influenza.

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Related Experiment Videos

Last Updated: Jun 4, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Area of Science:

  • Radiology
  • Medical Imaging
  • Computational Pathology

Background:

  • Pulmonary infections, such as novel H1N1 influenza, present diagnostic challenges on chest computed tomographic (CT) imaging.
  • Accurate quantification of lung abnormalities is crucial for assessing disease severity and progression.

Purpose of the Study:

  • To develop and validate a computer-assisted detection (CAD) method for identifying and measuring pulmonary abnormalities on chest CT.
  • To assess the utility of this CAD method in cases of infection, specifically novel H1N1 influenza.

Main Methods:

  • Texture analysis and support vector machine (SVM) classification were employed on 40 chest CT examinations.
  • The study included patients with H1N1 infection, normal controls, and patients with fibrosis to differentiate lung pathologies.

Main Results:

  • The CAD method demonstrated statistically significant differences in receiver-operating characteristic curves for detecting abnormal regions in H1N1 infection compared to normal lung and fibrosis.
  • Significant differences in texture features were identified among different infections, enabling the quantification of abnormal lung volumes on CT imaging.

Conclusions:

  • Texture analysis and SVM classification can effectively distinguish acute infections from chronic fibrosis on CT scans.
  • The method differentiates lesions with consolidative and ground-glass appearances and quantifies texture features, enhancing the precision of CT scoring for disease management.