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

7.3K
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...
7.3K

You might also read

Related Articles

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

Sort by
Same author

A Survey on Semantic Communications in Internet of Vehicles.

Entropy (Basel, Switzerland)·2025
Same author

Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels.

Sensors (Basel, Switzerland)·2025
Same author

Age of Information Analysis for Multi-Priority Queue and Non-Orthoganal Multiple Access (NOMA)-Enabled Cellular Vehicle-to-Everything in Internet of Vehicles.

Sensors (Basel, Switzerland)·2025
Same author

Joint Optimization of Age of Information and Energy Consumption in NR-V2X System Based on Deep Reinforcement Learning.

Sensors (Basel, Switzerland)·2024
Same author

Semantic Communication: A Survey of Its Theoretical Development.

Entropy (Basel, Switzerland)·2024
Same author

Deep Reinforcement Learning-Based Power Allocation for Minimizing Age of Information and Energy Consumption in Multi-Input Multi-Output and Non-Orthogonal Multiple Access Internet of Things Systems.

Sensors (Basel, Switzerland)·2023
Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026
Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026
Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026
Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026
Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026
Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Nov 2, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.1K

A lossless compression method for multi-component medical images based on big data mining.

Gangtao Xin1, Pingyi Fan2

  • 1The Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China.

Scientific Reports
|June 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel soft compression algorithm for medical images, leveraging big data mining to improve lossless compression. The new method enhances storage and bandwidth efficiency for remote medical systems.

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.7K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K

Related Experiment Videos

Last Updated: Nov 2, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.1K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.7K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K

Area of Science:

  • Medical Imaging
  • Data Compression
  • Big Data Analytics

Background:

  • Lossless compression of medical images is critical for efficient storage and transmission in remote diagnostic systems.
  • Medical images possess unique properties of lossless data and inherent similarity, which are key to effective compression.
  • Current compression methods may not fully exploit these properties, impacting storage and bandwidth requirements.

Purpose of the Study:

  • To develop a soft compression algorithm for multi-component medical images that accurately represents their fundamental structure.
  • To utilize big data mining techniques for creating an image codebook to identify basic image components.
  • To establish a general representation framework for image compression.

Main Methods:

  • Employed big data mining to construct an image codebook, identifying fundamental image components.
  • Proposed a novel soft compression algorithm tailored for multi-component medical images.
  • Developed a general representation framework for image compression.

Main Results:

  • The proposed soft compression algorithm effectively captures the fundamental structure of medical images.
  • The algorithm demonstrated superior compression ratios compared to established benchmarks like PNG and JPEG2000.
  • The approach optimizes storage space and communication bandwidth for medical imaging data.

Conclusions:

  • The developed soft compression algorithm offers a significant improvement in medical image compression.
  • This method effectively utilizes the lossless and similarity properties of medical images.
  • The algorithm provides a promising solution for enhancing the efficiency of remote medical systems and patient care.