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

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and the...
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...

You might also read

Related Articles

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

Sort by
Same author

Detecting Performance Drift in AI Models for Medical Image Analysis Using CUSUM Chart.

Journal of imaging informatics in medicine·2026
Same author

Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science.

Clinical pharmacology and therapeutics·2023
Same author

Characterization of mechanical stiffness using additive manufacturing and finite element analysis: potential tool for bone health assessment.

3D printing in medicine·2023
Same author

Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging: Translation to Patient Care.

JACC. Cardiovascular imaging·2023
Same author

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging.

Medical physics·2022
Same author

U-Net based deep learning bladder segmentation in CT urography.

Medical physics·2019

Related Experiment Video

Updated: Jun 25, 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 approach to nodule feature optimization on thin section thoracic CT.

Ravi Samala1, Wilfrido Moreno, Yuncheng You

  • 1College of Engineering, University of South Florida, Tampa, FL, USA.

Academic Radiology
|March 10, 2009
PubMed
Summary

This study optimized image feature selection for computer-aided diagnosis of lung nodules. Analyzing features individually and collectively improves classification accuracy for early lung cancer detection.

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

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery
07:30

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery

Published on: May 4, 2022

Related Experiment Videos

Last Updated: Jun 25, 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

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery
07:30

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery

Published on: May 4, 2022

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Radiology

Background:

  • Accurate representation of lung nodules is crucial for computer-aided diagnosis (CADx) systems.
  • Selecting optimal image features is essential for improving CADx performance in lung nodule classification.

Purpose of the Study:

  • To perform an analysis for the optimum selection of image features in the feature domain to represent lung nodules.
  • To implement feature selection into a classification module of a computer-aided diagnosis system for early lung cancer diagnosis.

Main Methods:

  • Utilized 42 regions of interest from 38 cases (nodule diameters 3-8.5 mm).
  • Computed 11 image features and analyzed their relationship with radiologist annotations using correlation coefficients, multiple regression, and principal-component analysis.
  • Employed an artificial neural network for benign and malignant nodule classification to test feature selection hypotheses.

Main Results:

  • Correlation coefficients between radiologist annotations and computed features ranged from 0.2693 to 0.5178.
  • Identified redundant features, with 3 features redundant in combined cases and 5 in single-class cases.
  • Achieved F-test values of 0.821 for malignant and 0.643 for benign nodules through combined analysis and artificial neural network classification.

Conclusions:

  • Optimal feature selection requires individual and collective analysis of each feature's impact on the CADx system.
  • This methodology enhances the generalization capability of classification modules for improved early lung cancer diagnosis.