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 Experiment Videos

Orientation correction for chest images.

E Pietka1, H K Huang

  • 1Department of Radiological Sciences, University of California, Los Angeles 90024-1721.

Journal of Digital Imaging
|August 1, 1992
PubMed
Summary
This summary is machine-generated.

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

[Effects of Shugan Jieyu capsules combined with Zolpidem on sleep quality and daytime cognitive function in insomnia with depressive symptoms patients].

Zhonghua yi xue za zhi·2025
Same author

HoG feature based detection of tissue deformations in ultrasound data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016
Same author

Watershed based intelligent scissors.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2015
Same author

Soft computing approach to 3D lung nodule segmentation in CT.

Computers in biology and medicine·2014
Same author

Medical imaging, PACS, and imaging informatics: retrospective.

Radiological physics and technology·2013
Same author

Medical imaging informatics simulators: a tutorial.

International journal of computer assisted radiology and surgery·2013
Same journal

Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios.

Journal of digital imaging·2023
Same journal

Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.

Journal of digital imaging·2023
Same journal

DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.

Journal of digital imaging·2023
Same journal

Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.

Journal of digital imaging·2023
Same journal

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

Journal of digital imaging·2023
Same journal

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Journal of digital imaging·2023
See all related articles

This study introduces an automated method to correctly orient computed radiography (CR) chest X-rays for radiologists. The system achieves a 95.4% accuracy rate in standardizing image positioning.

Area of Science:

  • Medical Imaging
  • Radiography
  • Image Processing

Background:

  • Radiologists require correctly oriented chest X-rays for accurate diagnosis.
  • Manual image rotation is time-consuming and prone to error.
  • Computed radiography (CR) systems generate digital chest images that may require orientation correction.

Purpose of the Study:

  • To develop and implement an automatic procedure for determining and correcting the orientation of CR chest images.
  • To standardize image orientation for improved radiologist workflow and diagnostic accuracy.

Main Methods:

  • An automated algorithm analyzes CR chest images (1000x1000 or 2000x2000 pixels).
  • The procedure involves determining spine orientation, locating upper extremities and subdiaphragm, and comparing lung areas.

Related Experiment Videos

  • Image rotation parameters are derived from these analyses to achieve a standard viewing position.

    • Main Results:

    • The automated procedure correctly rotates 95.4% of CR chest images.
    • The system has been successfully implemented in clinical settings at UCLA.
    • The method provides a reliable solution for standardizing CR chest image orientation.

    Conclusions:

    • The developed automatic procedure effectively orients CR chest images with high accuracy.
    • This automation enhances radiologist efficiency by providing consistently positioned images.
    • The system offers a valuable tool for improving diagnostic workflows in medical imaging departments.