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

7.9K
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...
7.9K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

732
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...
732
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

980
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...
980
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

237
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...
237

You might also read

Related Articles

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

Sort by
Same author

Measuring the Effect of Vision on the Synergy of Lower Extremity Muscles during Walking using Nonnegative Matrix Factorization (NNMF) Algorithm Method.

Applied bionics and biomechanics·2023
Same author

Automatic Generation of Structured Radiology Reports for Volumetric Computed Tomography Images Using Question-Specific Deep Feature Extraction and Learning.

Journal of medical signals and sensors·2021
Same author

Multi-level fuzzy min-max neural network classifier.

IEEE transactions on neural networks and learning systems·2014
See all related articles

Related Experiment Video

Updated: Jan 3, 2026

Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

13.6K

Generate Structured Radiology Report from CT Images Using Image Annotation Techniques: Preliminary Results with Liver

Samira Loveymi1, Mir Hossein Dezfoulian1, Muharram Mansoorizadeh2

  • 1Computer Engineering Department, Bu-Ali Sina University, Shahid Fahmideh blvd., Hamedan, Iran.

Journal of Digital Imaging
|November 16, 2019
PubMed
Summary

This study introduces a medical annotation system for liver CT scans, achieving 93.1% accuracy in predicting radiological annotations. The system uses specific features for different report sections, enhancing diagnostic decision-making.

Keywords:
Computed tomographyDeep local binary patternLiver CT imagesMedical image annotation

More Related Videos

Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models
09:23

Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models

Published on: September 22, 2023

3.5K
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.6K

Related Experiment Videos

Last Updated: Jan 3, 2026

Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

13.6K
Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models
09:23

Author Spotlight: Enhancing Transplantation Research Through MicroCT Angiography in Murine Models

Published on: September 22, 2023

3.5K
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.6K

Area of Science:

  • Medical Imaging
  • Radiology
  • Computer Vision

Background:

  • Medical annotation systems extract information from radiology images for automatic reasoning and diagnosis.
  • Generating structured reports for liver CT images requires predicting specific semantic content related to liver, lesions, and vessels.

Purpose of the Study:

  • To develop a computerized framework for predicting radiological annotations from liver CT images with high accuracy.
  • To generate structured reports by predicting high-level semantic content and identifying relationships between semantic concepts and low-level image features.

Main Methods:

  • Implemented a framework combining state-of-the-art low-level imaging features.
  • Proposed a novel deep local binary pattern (DLBP) feature for multi-slice CT image analysis.
  • Utilized multi-class support vector machine (SVM) and random subspace (RS) ensemble learning for modeling.

Main Results:

  • Achieved a high prediction accuracy of 93.1% for radiological annotations.
  • Demonstrated that specific features are more suitable for specific annotation groups.
  • The novel DLBP feature improved performance in CT image analysis.

Conclusions:

  • The developed framework accurately predicts radiological annotations for liver CT images, enabling structured report generation.
  • Tailoring feature extraction for specific annotation groups enhances prediction accuracy.
  • The proposed DLBP feature shows promise for improving medical image analysis.