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Related Concept Videos

Doppler Effect - II01:05

Doppler Effect - II

The Doppler effect has several practical, real-world applications. For instance, meteorologists use Doppler radars to interpret weather events based on the Doppler effect. Typically, a transmitter emits radio waves at a specific frequency toward the sky from a weather station. The radio waves bounce off the clouds and precipitation and travel back to the weather station. The radio frequency of the waves reflected back to the station appears to decrease if the clouds or precipitation are moving...
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
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Related Experiment Video

Updated: May 22, 2026

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Published on: August 23, 2017

Stochastic region competition algorithm for Doppler sonography segmentation.

Fang-Cheng Yeh1, Jie-Zhi Cheng, Yi-Hong Chou

  • 1Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Medical Physics
|May 8, 2012
PubMed
Summary

A new stochastic region competition algorithm accurately segments mass lesions in Doppler sonography, even with low image quality. This automated method matches the reproducibility of manual segmentation, offering a clinical alternative.

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Blood Flow Imaging with Ultrafast Doppler
05:57

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Related Experiment Videos

Last Updated: May 22, 2026

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Published on: August 23, 2017

Blood Flow Imaging with Ultrafast Doppler
05:57

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Area of Science:

  • Medical imaging
  • Ultrasound technology
  • Image processing

Background:

  • Doppler sonography is crucial for diagnosing various medical conditions.
  • Accurate segmentation of sonographic images, particularly mass lesions, is essential for diagnosis and monitoring.
  • Manual segmentation is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To introduce a novel probabilistic image segmentation algorithm, stochastic region competition.
  • To apply this algorithm for automated segmentation in Doppler sonography.
  • To evaluate the algorithm's performance against manual delineations.

Main Methods:

  • The algorithm maximizes a posteriori probability, incorporating histogram likelihood, gradient likelihood, and spatial priors.
  • Optimization is achieved using a modified expectation-maximization (EM) algorithm for enhanced efficiency and to prevent local optima.
  • The method was validated on 155 color Doppler sonograms.

Main Results:

  • The algorithm successfully segmented mass lesions in low-quality images with color-encoded Doppler interference.
  • Quantitative analysis showed algorithm-generated boundaries were statistically comparable to manual delineations in terms of average distance and overlapping area ratio.
  • Reproducibility tests confirmed the algorithm's results are statistically reliable.

Conclusions:

  • The stochastic region competition algorithm provides accurate and reproducible segmentation for Doppler sonography.
  • This automated approach can potentially replace manual delineation tasks in clinical settings.
  • The algorithm demonstrates utility in segmenting challenging sonographic images, improving efficiency and consistency.