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

Fast Fourier Transform01:10

Fast Fourier Transform

1.2K
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
1.2K
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

1.2K
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...
1.2K
Discrete Fourier Transform01:15

Discrete Fourier Transform

1.1K
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
1.1K
Relation of DFT to z-Transform01:20

Relation of DFT to z-Transform

926
The Discrete Fourier Transform (DFT) is a crucial tool for analyzing the frequency content of discrete-time signals. It converts a sequence of N samples from the time domain into its corresponding sequence in the frequency domain, where each sample represents a specific frequency component.
To understand how the DFT works, it's helpful to consider the z-transform, which is a method for representing discrete sequences in the complex frequency domain. The z-transform involves summing the...
926
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

1.4K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
1.4K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

876
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
876

You might also read

Related Articles

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

Sort by
Same author

Systematic literature review and narrative synthesis of the use of natural language processing to triage outpatient referrals.

Frontiers in health services·2026
Same author

The Alberta Quality Assessment Tool: Risk of Bias (AQAT:RoB) for the Evaluation of Medical Large Language Model Question-Answer Studies: Development and Pilot Validation.

Journal of medical Internet research·2026
Same author

The Alberta Risk of Bias Assessment Tool (AQAT:RoB) for the Evaluation of Medical Large Language Model Question-Answer Studies: Development and Pilot Validation.

Journal of medical Internet research·2026
Same author

Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients.

Frontiers in neuroimaging·2023
Same author

A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study.

Journal of medical Internet research·2022
Same author

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

Journal of medical imaging (Bellingham, Wash.)·2020

Related Experiment Video

Updated: Apr 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.9K

Texture analysis of images using a two-dimensional fast time-frequency transform.

Chun Hing Cheng1, Pyari Mohan Pradhan2, Joseph Ross Mitchell1

  • 1Mayo Clinic , Department of Radiology, 13400 East Shea Boulevard, Scottsdale, Arizona 85259, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|July 10, 2015
PubMed
Summary
This summary is machine-generated.

A new fast time-frequency transform (FTFT-2D) significantly speeds up medical image texture analysis by improving upon the two-dimensional S-transform (ST-2D). This method reduces computation time and storage needs for analyzing medical images.

Keywords:
S-transformmedical image processingtime-frequency representationtime–time-transform

More Related Videos

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
11:15

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

26.5K
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.8K

Related Experiment Videos

Last Updated: Apr 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.9K
A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
11:15

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

26.5K
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.8K

Area of Science:

  • Medical Image Processing
  • Signal Analysis
  • Computational Imaging

Background:

  • The two-dimensional S-transform (ST-2D) is valuable for medical image analysis.
  • However, its computational complexity and storage demands limit its application, especially for texture analysis.

Purpose of the Study:

  • To introduce a novel two-dimensional fast time-frequency transform (FTFT-2D).
  • To address the computational and storage limitations of ST-2D in medical imaging.

Main Methods:

  • Development of the FTFT-2D algorithm for instantaneous and accurate local spectrum computation.
  • Implementation of a compressed ST-2D output generation.
  • Demonstration of memory-efficient and adaptive processing capabilities.

Main Results:

  • The FTFT-2D significantly reduces computation time for local spectrum generation.
  • It offers a compressed representation of ST-2D, lowering storage requirements.
  • The transform is memory-efficient and adaptable to user-specific needs.

Conclusions:

  • FTFT-2D provides an efficient alternative to ST-2D for medical image texture analysis.
  • The method enhances the feasibility of using time-frequency representations in resource-constrained environments.
  • FTFT-2D is suitable for various user-specific medical imaging applications.