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Related Concept Videos

Fast Fourier Transform01:10

Fast Fourier Transform

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...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Downsampling01:20

Downsampling

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...
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...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
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.
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Related Experiment Video

Updated: May 10, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

A fast bilateral filter with application to artefact reduction.

Dangguo Shao1, Ming Zhong, Dong C Liu

  • 1a Faculty of Information Engineering and Automation, KunMing University of Science and Technology , KunMing , P.R. China.

Computer Methods in Biomechanics and Biomedical Engineering
|July 2, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a fast bilateral filter using local histograms to reduce noise in medical ultrasound elastography, improving image quality and preserving tissue structure for better diagnostic accuracy.

Keywords:
artefact reductionbilateral filterlocal histogramultrasound elastography

Related Experiment Videos

Last Updated: May 10, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Signal Processing

Background:

  • Medical ultrasound elastography visualizes tissue stiffness but is prone to noise from decorrelation and amplitude modulation errors.
  • Artefact noise degrades the quality and diagnostic reliability of elastographic images.
  • Accurate tissue stiffness assessment is crucial for diagnosing various medical conditions.

Purpose of the Study:

  • To develop and evaluate a novel, fast bilateral filter for reducing artefact noise in ultrasonic elastography.
  • To enhance the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of elastographic images.
  • To maintain the integrity of tissue structures while suppressing noise.

Main Methods:

  • A fast bilateral filter algorithm was developed, incorporating a local histogram to accelerate processing.
  • The filter was derived from conventional bilateral filtering techniques.
  • The proposed method was validated using both simulated data and phantom testing.

Main Results:

  • The proposed fast bilateral filter effectively reduced artefact noise in ultrasonic elastography.
  • The filter demonstrated the ability to preserve essential tissue structures.
  • Significant improvements in elastographic signal-to-noise ratio and contrast-to-noise ratio were observed.

Conclusions:

  • The fast bilateral filter based on local histograms is an effective method for enhancing ultrasonic elastography quality.
  • This technique offers a promising solution for reducing noise and improving diagnostic accuracy in medical ultrasound.
  • The method successfully balances noise reduction with the preservation of critical tissue information.