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

Pulmonary Hypertension: Classification and Pathogenesis01:30

Pulmonary Hypertension: Classification and Pathogenesis

Pulmonary hypertension (PH) is a severe health condition in which the mean pulmonary arterial pressure increases to 25 mmHg or more, even when the body is at rest. This high pressure in the blood vessels that transport blood from the heart to the lungs can cause various symptoms, including shortness of breath, can lead to right heart failure, and significantly affect the overall quality of life.
There are various classifications for PH, each relating to different underlying causes and also...
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Advancing and integrating climate and health policy in the United Kingdom: Insights from national policy actors.

The journal of climate change and health·2026
Same author

Perceived eye care risk and safety issues identified by optometrists in Scotland: a focus group study.

BMJ open quality·2026
Same author

Student Progress Dashboard Versus Legacy Academic Monitoring for Medical Student Support: A Consolidated Framework for Implementation Research-Guided Evaluation.

Cureus·2026
Same author

Impact of Pollution on Mental Health: A Systematic Review of Associations, Methodological Challenges, and Future Directions.

Health science reports·2026
Same author

Evaluating Antibody Quality via Simultaneous Size and Charge Measurement with Single Protein Oscillators.

Analytical chemistry·2026
Same author

Point2SSM++: Self-supervised learning of anatomical shape models from point clouds.

Medical image analysis·2026

Related Experiment Video

Updated: Jun 8, 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

Toward precise pulmonary nodule descriptors for nodule type classification.

Amal Farag1, Shireen Elhabian, James Graham

  • 1Department of Electrical and Computer Engineering, University of Louisville Medical Imaging Division, Jewish Hospital, Louisville, KY, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 1, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for classifying lung nodules in low-dose CT scans using feature extraction. The Scale Invariant Feature Transform (SIFT) with Principal Component Analysis (PCA) demonstrated robust and precise lung nodule classification.

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: Jun 8, 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

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • Accurate lung nodule classification is crucial for early lung cancer detection.
  • Existing methods for analyzing low-dose CT (LDCT) scans have limitations in nodule characterization.

Purpose of the Study:

  • To develop and evaluate a novel framework for classifying lung nodules in LDCT images.
  • To compare the effectiveness of Scale Invariant Feature Transform (SIFT) and an adapted Daugman's Iris Recognition algorithm for nodule classification.

Main Methods:

  • Feature extraction from lung nodules using SIFT and an adapted Daugman's Iris Recognition algorithm.
  • Dimensionality reduction of SIFT descriptors via Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
  • Classification of nodules into juxta, well-circumscribed, vascularized, and pleural-tail categories.

Main Results:

  • The adapted Daugman algorithm using complex Gabor wavelets showed improvements over binary iris codes.
  • Binarized nodule responses were found to be insufficient for accurate classification due to lack of texture concentration.
  • SIFT algorithm, when projected using PCA, exhibited robustness and precision in classifying lung nodules.

Conclusions:

  • The proposed framework effectively classifies lung nodules in LDCT scans.
  • SIFT combined with PCA offers a precise and robust approach for lung nodule classification, outperforming binarized methods.
  • The findings highlight the importance of advanced feature extraction techniques in medical image analysis.