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

9.2K
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...
9.2K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

3.0K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
3.0K
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

14.8K
Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
14.8K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

408
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....
408
Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

1.8K
Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
1.8K
Deconvolution01:20

Deconvolution

651
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...
651

You might also read

Related Articles

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

Sort by
Same authorSame journal

Creation of a Standardized Myocardial Perfusion Single-photon Emission Computed Tomography Database to Enhance Coronary Artery Disease Research.

Journal of medical signals and sensors·2026
Same author

A population readout of extrastriate activity reveals biased and smoothed temporal representations across saccades.

bioRxiv : the preprint server for biology·2026
Same author

Deep learning-based diagnostic classification of multiple sclerosis using multicenter optical coherence tomography data.

Experimental eye research·2026
Same author

Isfahan Artificial Intelligence Event 2024, Challenge I: Respiratory Depression Detection.

Journal of medical signals and sensors·2026
Same author

Diagnosing Multiple Sclerosis from Magnetic Resonance Imaging Images: Highlights from the Second Isfahan Artificial Intelligence Event 2024.

Journal of medical signals and sensors·2026
Same author

Isfahan Artificial Intelligent 2024 Competitions.

Journal of medical signals and sensors·2026

Related Experiment Video

Updated: Mar 1, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

10.0K

Speckle Noise Reduction in Optical Coherence Tomography Using Two-dimensional Curvelet-based Dictionary Learning.

Mahdad Esmaeili1, Alireza Mehri Dehnavi1, Hossein Rabbani1

  • 1Department of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran.

Journal of Medical Signals and Sensors
|May 30, 2017
PubMed
Summary

This study introduces a novel 2D curvelet-based K-SVD algorithm to reduce speckle noise in optical coherence tomography (OCT) images. The method enhances intra-retinal layer contrast and improves image quality for better interpretation.

Keywords:
Curvelet transformdictionary learningoptical coherence tomographyspeckle noise

More Related Videos

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

12.0K
Author Spotlight: Advancing Neonatal Cardiac Diagnostics with Echocardiography-Derived Blood Speckle Imaging
07:13

Author Spotlight: Advancing Neonatal Cardiac Diagnostics with Echocardiography-Derived Blood Speckle Imaging

Published on: December 22, 2023

2.0K

Related Experiment Videos

Last Updated: Mar 1, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

10.0K
Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

12.0K
Author Spotlight: Advancing Neonatal Cardiac Diagnostics with Echocardiography-Derived Blood Speckle Imaging
07:13

Author Spotlight: Advancing Neonatal Cardiac Diagnostics with Echocardiography-Derived Blood Speckle Imaging

Published on: December 22, 2023

2.0K

Area of Science:

  • Biomedical Imaging
  • Medical Image Processing
  • Ophthalmology

Background:

  • Speckle noise significantly hinders the interpretation of high-speed optical coherence tomography (OCT) images.
  • Accurate analysis of intra-retinal layers in OCT scans is crucial for diagnosing various eye conditions.

Purpose of the Study:

  • To develop and validate a new algorithm for speckle noise reduction and contrast enhancement in 2D spectral-domain OCT images.
  • To improve the visualization and diagnostic capabilities of OCT imaging for retinal diseases.

Main Methods:

  • A two-dimensional (2D) curvelet transform is applied to noisy OCT images.
  • Adaptive data-driven thresholding is used on curvelet sub-bands, followed by K-SVD dictionary learning for denoising.
  • Coefficient matrices are modified to enhance intra-retinal layers while suppressing noise.

Main Results:

  • The proposed algorithm effectively reduces speckle noise in 100 public OCT B-scans.
  • Contrast-to-noise ratio improved from 1.27 to 5.12.
  • Mean-to-standard deviation ratio increased from 3.20 to 14.41.

Conclusions:

  • The 2D curvelet-based K-SVD algorithm offers a significant advancement in OCT image processing.
  • This method enhances image quality, aiding in the detection and analysis of retinal pathologies, including age-related macular degeneration.