Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Upsampling

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...

You might also read

Related Articles

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

Sort by

Same author

Slipped capital femoral epiphysis associated with vitamin D deficiency: a series of 15 cases.

The bone & joint journal·2013

Same author

The genus Gelsemium: An update.

Pharmacognosy reviews·2012

Same author

Function after total calcanectomy for malignant tumor in a child: is complex reconstruction necessary?

The Journal of foot and ankle surgery : official publication of the American College of Foot and Ankle Surgeons·2011

Same author

Noninvasive ultrasound image guided surface wave method for measuring the wave speed and estimating the elasticity of lungs: A feasibility study.

Ultrasonics·2010

Same author

Shear wave dispersion ultrasonic vibrometry for measuring prostate shear stiffness and viscosity: an in vitro pilot study.

IEEE transactions on bio-medical engineering·2010

Same author

A Kramers-Kronig-based quality factor for shear wave propagation in soft tissue.

Physics in medicine and biology·2009

Search research articles

Related Experiment Video

Updated: Jul 8, 2026

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

Adaptive speckle reduction filter for log-compressed B-scan images.

V Dutt¹, J F Greenleaf

¹Ultrasound Res. Lab., Mayo Clinic, Rochester, MN.

IEEE Transactions on Medical Imaging

|January 1, 1996

Summary

This study introduces a new statistical model for speckle in log-compressed ultrasound images. This model quantifies speckle, enabling adaptive filter design for improved image clarity.

Area of Science:

Medical Imaging
Signal Processing
Statistical Modeling

Background:

Speckle noise in ultrasound B-scan images degrades image quality.
Existing statistical models do not account for log compression used in clinical systems.
Log compression reduces dynamic range and enhances weak signals in ultrasound imaging.

Purpose of the Study:

To develop a statistical model for speckle in log-compressed ultrasound echo envelope.
To derive a parameter for quantifying speckle formation in these images.
To utilize this parameter for adaptive speckle reduction filters.

Main Methods:

Utilized the K-distribution statistical model for the echo envelope.
Derived a speckle quantification parameter from log-compressed echo image statistics.

More Related Videos

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Related Experiment Videos

Last Updated: Jul 8, 2026

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Applied an unsharp masking filter adapted using the derived parameter.

Main Results:

Successfully quantified speckle formation in log-compressed ultrasound images.
Demonstrated adaptive speckle reduction using the developed method.
Validated the filter's effectiveness on phantom and in-vivo abdominal images.

Conclusions:

The K-distribution model accurately describes speckle in log-compressed ultrasound images.
The derived speckle quantification parameter enables effective adaptive speckle reduction.
This approach improves image quality for clinical ultrasound applications.