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

7.5K
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...
7.5K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

876
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...
876

You might also read

Related Articles

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

Sort by
Same author

Retraction Note: An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model.

Soft computing·2026
Same author

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment.

Sensors (Basel, Switzerland)·2024
Same author

An emerging paradigms on cervical cancer screening methods and devices for clinical trails.

Frontiers in public health·2022
Same author

RETRACTED ARTICLE: An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model.

Soft computing·2022
Same author

Indoor Positioning Systems: A Blessing for Seamless Object Identification, Monitoring, and Tracking.

Frontiers in public health·2022

Related Experiment Video

Updated: Apr 23, 2026

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.9K

Projection-based medical image compression for telemedicine applications.

Sujitha Juliet1, Elijah Blessing Rajsingh, Kirubakaran Ezra

  • 1Department of Information Technology, Karunya University, Coimbatore, India, sujitha_juliet@yahoo.com.

Journal of Digital Imaging
|September 20, 2014
PubMed
Summary

This study introduces an efficient medical image compression technique for telemedicine, utilizing the Radon transform and SPIHT encoding. The method achieves competitive compression ratios and image quality, addressing data challenges in remote healthcare.

More Related Videos

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone
09:26

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone

Published on: January 2, 2020

19.4K
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

2.9K

Related Experiment Videos

Last Updated: Apr 23, 2026

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.9K
Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone
09:26

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone

Published on: January 2, 2020

19.4K
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

2.9K

Area of Science:

  • Medical Imaging
  • Telemedicine
  • Data Compression

Background:

  • Advancements in medical imaging and telemedicine highlight the need for efficient data compression.
  • Existing storage and communication technologies face challenges with large medical datasets.
  • Effective compression is crucial for seamless telemedicine applications.

Purpose of the Study:

  • To propose an efficient medical image compression method tailored for telemedicine applications.
  • To leverage the directional information representation capabilities of the Radon transform.
  • To enhance the performance of medical data compression.

Main Methods:

  • Utilizes the Radon transform for its effectiveness in representing directional information.
  • Employs periodic re-ordering of Radon projections, minimizing interpolation and preserving pixel intensities.
  • Converts 2D image processing into independent 1D tasks on projections.
  • Encodes resultant Radon coefficients using the Set Partitioning in Hierarchical Trees (SPIHT) algorithm.

Main Results:

  • The proposed method demonstrates competitive performance against conventional and state-of-the-art compression techniques.
  • Achieves favorable compression ratios.
  • Maintains high peak signal-to-noise ratio (PSNR) values.
  • Shows efficient computational time.

Conclusions:

  • The proposed Radon transform-based method offers an efficient solution for medical image compression in telemedicine.
  • It effectively balances compression ratio, image fidelity (PSNR), and computational efficiency.
  • This approach contributes to overcoming data handling challenges in telemedicine.