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

Deconvolution01:20

Deconvolution

316
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...
316
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.1K
Reducing Line Loss01:18

Reducing Line Loss

220
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...
220
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

409
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
409
Downsampling01:20

Downsampling

311
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...
311
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.9K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.9K

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 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

694

A Method of CT Image Denoising Based on Residual Encoder-Decoder Network.

Yali Liu1

  • 1Faculty of Economics and Management, Shangluo University, Shaanxi, Shangluo 726000, China.

Journal of Healthcare Engineering
|October 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved residual encoder-decoder network for low-dose computed tomography (CT) image denoising. The method enhances image quality and diagnostic accuracy by reducing noise and artifacts while preserving texture details.

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

3.0K

Related Experiment Videos

Last Updated: Oct 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

694
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

3.0K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Low-dose computed tomography (CT) reduces radiation risk but introduces noise and artifacts, hindering medical diagnosis.
  • Existing denoising methods struggle to preserve image texture while reducing noise in CT scans.
  • The difficulty in modeling statistical image features limits the effectiveness of current CT image processing techniques.

Purpose of the Study:

  • To develop an advanced CT image-denoising method to overcome limitations of existing techniques.
  • To improve the diagnostic quality of low-dose CT images by effectively removing noise and artifacts.
  • To enhance the preservation of detailed image texture in denoised CT scans.

Main Methods:

  • An improved residual encoder-decoder network incorporating recursion for enhanced efficiency and reduced complexity.
  • Simultaneous input of original CT images and post-recursion results to recycle a shallow encoder-decoder network.
  • Integration of root-mean-square error and perceptual loss functions to ensure texture fidelity.
  • Optimization of tissue processing using clustering segmentation to address residual artifacts.

Main Results:

  • The proposed method demonstrates superior performance compared to WGAN in average post-denoising Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on the TCGA-COAD dataset.
  • The recursive approach significantly lowers algorithm complexity and execution time.
  • The method shows a notable improvement over the standard RED-CNN (Residual Encoder-Decoder Network) for CT image denoising.
  • Experimental results confirm the method's applicability in practical scenarios.

Conclusions:

  • The improved residual encoder-decoder network effectively denoises low-dose CT images, preserving crucial texture details.
  • The integration of recursion and advanced loss functions enhances denoising performance and efficiency.
  • This method offers a significant advancement for CT image analysis, outperforming existing techniques in key metrics.
  • The developed technique is suitable for real-world clinical applications requiring high-quality diagnostic images.