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

Reducing Line Loss01:18

Reducing Line Loss

225
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
225
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
Deconvolution01:20

Deconvolution

335
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
335
Downsampling01:20

Downsampling

330
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
330
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

169
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
169
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

197
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
197

You might also read

Related Articles

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

Sort by
Same journal

RETRACTION: An IoMT-Based Approach for Real-Time Monitoring Using Wearable Neuro-Sensors.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency in IoHT Systems.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Multi-Chaos-Based Lightweight Image Encryption-Compression for Secure Occupancy Monitoring.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Image Risk Assessment of the Thyroid Cancer Model Based on Discriminant Analysis and the Value of TAP and CEA Combined Detection.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Meta-Analysis of the Prognostic Value of Narcotrend Monitoring of Different Depths of Anesthesia and Different Bispectral Index (BIS) Values for Cognitive Dysfunction after Tumor Surgery in Elderly Patients.

Journal of healthcare engineering·2026
Same journal

Correction to "Representation of Differential Learning Method for Mitosis Detection".

Journal of healthcare engineering·2026

Related Experiment Video

Updated: Oct 25, 2025

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

14.6K

Edge-Preserving Median Filter and Weighted Coding with Sparse Nonlocal Regularization for Low-Dose CT Image Denoising

Quan Yuan1, Zhenyun Peng1, Zhencheng Chen1

  • 1School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China.

Journal of Healthcare Engineering
|August 6, 2021
PubMed
Summary
This summary is machine-generated.

A novel denoising algorithm combining an edge-preserving median filter and sparse nonlocal regularization effectively removes mixed noise in CT images. This method significantly improves image quality, showing potential for low-dose CT applications.

More Related Videos

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: Oct 25, 2025

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

14.6K
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
  • Image Processing
  • Computational Science

Background:

  • Computed Tomography (CT) imaging is crucial for medical diagnosis.
  • Image noise, particularly impulse and Gaussian noise, degrades CT image quality.
  • Effective noise reduction is vital for accurate interpretation and diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for denoising CT images corrupted by mixed noise.
  • To assess the performance of the proposed algorithm against existing methods.
  • To explore the potential application of the algorithm in low-dose CT imaging.

Main Methods:

  • An edge-preserving median filter (EP median filter) was employed to address impulse noise.
  • A sparse nonlocal regularization algorithm with weighted coding (WESNR) was utilized for mixed noise removal.
  • Performance was quantitatively evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).

Main Results:

  • The combined EP median filter and WESNR algorithm demonstrated superior denoising performance compared to using either method alone.
  • Significant improvements in PSNR and SSIM values were observed across various noise proportions in both Shepp-Logan and CT images.
  • The proposed algorithm effectively preserved image edges while reducing noise.

Conclusions:

  • The integrated denoising approach shows significant potential for enhancing the quality of low-dose CT images.
  • This method offers a promising solution for improving diagnostic accuracy in CT imaging.
  • The synergistic effect of edge-preserving filtering and sparse regularization is key to its effectiveness.