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

Postsurgical detection of glioma recurrence using MRI radiomics.

Neuro-oncology advances·2026
Same author

A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.

NeuroImage. Clinical·2026
Same author

Fully automatic left ventricle segmentation in [Formula: see text]Rb PET/CT Using a semi-supervised nnU-net.

EJNMMI research·2026
Same author

Impact of CT dose on AI performance: A comparison of radiomics, deep, and foundation models in a multicentric anthropomorphic phantom study.

Medical physics·2026
Same author

A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis.

Multiple sclerosis (Houndmills, Basingstoke, England)·2026
Same author

Performance of AI vs radiology residents in the detection of intracranial hemorrhage on emergency CT: a real-world analysis.

European radiology·2026

Related Experiment Video

Updated: May 22, 2026

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames.

Adrien Depeursinge1, Dimitri Van de Ville, Alexandra Platon

  • 1MedGIFT Group, Business Information Systems, University of Applied Sciences Western Switzerland, Sierre 3960, Switzerland. adrien.depeursinge@hevs.ch

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|May 17, 2012
PubMed
Summary
This summary is machine-generated.

We developed new texture descriptors for lung tissue patterns in CT scans. This method achieves 76.9% accuracy in classifying five lung tissue types, aiding in medical diagnosis.

More Related Videos

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment
05:56

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment

Published on: August 9, 2024

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Related Experiment Videos

Last Updated: May 22, 2026

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment
05:56

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment

Published on: August 9, 2024

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

  • Medical imaging analysis
  • Radiology
  • Computer-aided diagnosis

Background:

  • Characterizing lung tissue patterns in high-resolution computed tomography (HRCT) is crucial for diagnosing various pulmonary diseases.
  • Existing texture analysis methods may lack robustness to variations in scale, orientation, and position of lung textures.
  • Developing invariant descriptors is essential for reliable automated analysis of complex medical images.

Purpose of the Study:

  • To propose novel, near-affine-invariant texture descriptors for lung tissue characterization in HRCT images.
  • To evaluate the performance of these descriptors in classifying different types of lung tissue.
  • To assess the clinical relevance of the proposed method through a leave-one-patient-out cross-validation strategy.

Main Methods:

  • Derivation of texture descriptors from isotropic wavelet frames to achieve near-affine invariance.
  • Integration of these descriptors with complementary gray-level histograms for enhanced feature representation.
  • Application of a classification model using leave-one-patient-out cross-validation on five distinct lung tissue classes.

Main Results:

  • The proposed near-affine-invariant texture descriptors demonstrated effectiveness in characterizing lung tissue patterns.
  • The combined approach achieved a global classification accuracy of 76.9%.
  • Balanced precision was obtained across the five classified lung tissue categories.

Conclusions:

  • The developed texture descriptors offer a robust method for analyzing lung tissue in HRCT imaging.
  • The high classification accuracy suggests potential for clinical application in diagnosing lung conditions.
  • Near-affine invariance enhances the reliability of texture analysis for nondeterministic patterns in medical imaging.