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

4.3K
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...
4.3K

You might also read

Related Articles

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

Sort by
Same author

A hybrid optimized framework with energy shape prior segmentation for brain tumor detection in MRI images.

Digital health·2026
Same author

SCAG-Net: Automated Brain Tumor Prediction from MRI Using Cuttlefish-Optimized Attention-Based Graph Networks.

Diagnostics (Basel, Switzerland)·2026
Same author

Pharmacokinetics and molecular-level insights into 5-Methyl-3-(trifluoromethyl)-1H-pyrazole for anticancer action: Spectroscopic profiling, solvent interactions, topological analysis and ADME-QSAR predictions.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2025
Same author

AI-IoT based smart agriculture pivot for plant diseases detection and treatment.

Scientific reports·2025
Same author

Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum.

PeerJ. Computer science·2025
Same author

Hybrid Prairie Dog and Dwarf Mongoose optimization algorithm-based application placement and resource scheduling technique for fog computing environment.

Scientific reports·2025
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2025

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

Coefficient-Shuffled Variable Block Compressed Sensing for Medical Image Compression in Telemedicine Systems.

R Monika1, Samiappan Dhanalakshmi1, Narayanamoorthi Rajamanickam2

  • 1Department of ECE, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur 603203, Tamilnadu, India.

Bioengineering (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel medical image compression method, coefficient shuffling variable block-based compressed sensing (CSEM-VBCS), for efficient data handling. CSEM-VBCS enhances reconstruction quality and compression ratios, crucial for telemedicine and remote patient monitoring.

Keywords:
block compressive sensingcoefficient shufflingcompressive sensingmedical imagingtelemedicine

More Related Videos

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

534
X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.4K

Related Experiment Videos

Last Updated: Jun 6, 2025

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

534
X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.4K

Area of Science:

  • Medical Imaging
  • Signal Processing
  • Data Compression

Background:

  • Medical imaging is vital for diagnosing conditions, but generates large datasets requiring compression for efficient analysis and transmission.
  • Existing Compressed Sensing (CS) methods, including block-based CS (BCS), face challenges in high-quality image reconstruction due to random sampling.
  • Effective compression is essential for managing large volumes of medical data, especially in long-term patient monitoring.

Purpose of the Study:

  • To introduce a novel Compressed Sensing (CS) method, coefficient shuffling variable BCS (CSEM-VBCS), for medical image compression.
  • To improve image reconstruction quality and achieve higher compression ratios compared to existing techniques.
  • To address the limitations of random sampling in conventional CS and BCS methods.

Main Methods:

  • Developed a novel CS method, CSEM-VBCS, utilizing an energy matrix and coefficient shuffling.
  • Applied the CSEM-VBCS method to compress diverse medical images with balanced sparsity.
  • Evaluated performance metrics against contemporary state-of-the-art compression techniques.

Main Results:

  • The proposed CSEM-VBCS method demonstrated a substantial compression ratio and good reconstruction quality.
  • Experimental evaluations showed remarkable enhancement in performance metrics compared to existing methods.
  • CSEM-VBCS effectively prioritizes regions of interest through coefficient shuffling, improving compression without compromising image quality.

Conclusions:

  • CSEM-VBCS offers a superior approach to medical image compression, balancing compression ratio and reconstruction quality.
  • The method is particularly beneficial for telemedicine applications, overcoming bandwidth limitations for high-resolution medical image transmission.
  • CSEM-VBCS enhances the efficiency of remote patient monitoring and diagnosis through faster data acquisition and reduced redundancy.