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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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 sampling...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Convergence of Fourier Series01:21

Convergence of Fourier Series

The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Fischer Projections02:18

Fischer Projections

Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines. While...
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...

You might also read

Related Articles

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

Sort by
Same author

FOXP3-stained image analysis for follicular lymphoma: Optimal adaptive thresholding with maximal nucleus coverage.

Proceedings of SPIE--the International Society for Optical Engineering·2017
Same author

WE-E-217A-02: Methodologies for Evaluation of Standalone CAD System Performance.

Medical physics·2017
Same author

Treatment response assessment of head and neck cancers on CT using computerized volume analysis.

AJNR. American journal of neuroradiology·2010
Same author

Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.

IEEE transactions on medical imaging·2002
Same author

Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses.

Medical physics·2002
Same author

Selection of an optimal neural network architecture for computer-aided detection of microcalcifications--comparison of automated optimization techniques.

Medical physics·2001

Related Experiment Video

Updated: Jul 8, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Reconstruction from projections under time-frequency constraints.

B Sahiner1, A E Yagle

  • 1Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI.

IEEE Transactions on Medical Imaging
|January 1, 1995
PubMed
Summary
This summary is machine-generated.

This study introduces time-frequency filtering to reduce noise in computed tomography (CT) images. This novel method preserves important image features better than traditional filtering techniques.

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Related Experiment Videos

Last Updated: Jul 8, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

  • Medical Imaging
  • Signal Processing
  • Image Analysis

Background:

  • Low-pass filtering of computed tomography (CT) images can reduce noise but may obscure critical image features.
  • Image features are often more readily identified and processed in the time-frequency domain.

Purpose of the Study:

  • To develop and evaluate a spatially varying filtering technique for noisy CT images using time-frequency distributions.
  • To compare the effectiveness of filtering projection data before reconstruction versus filtering the reconstructed image directly.

Main Methods:

  • Utilized time-frequency distributions for spatially varying filtering of noisy CT images.
  • Constrained time-frequency representation coefficients to be zero in specific regions.
  • Applied filtering to projection data prior to reconstruction and directly to reconstructed images.
  • Minimized criteria such as deterministic minimum weighted perturbation or stochastic minimum mean-square error.

Main Results:

  • The proposed time-frequency filtering method demonstrated improved results compared to standard linear spatially invariant filtering.
  • Spatially varying filtering effectively reduced noise while preserving important image features.
  • Both filtering projection data and filtering reconstructed images showed benefits.

Conclusions:

  • Time-frequency distributions offer a powerful tool for spatially varying filtering of CT images.
  • This approach enhances noise reduction in CT imaging while maintaining diagnostic image quality.
  • The method provides a superior alternative to conventional filtering techniques for noisy CT data.