Principles of speckle tracking

Principles of speckle tracking

For access to this article, please select a purchase option:

Buy chapter PDF
(plus tax if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Handbook of Speckle Filtering and Tracking in Cardiovascular Ultrasound Imaging and Video — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A vast amount of speckle tracking techniques has been developed to extract tissue motion from a time series of medical US images. The purpose of this chapter is to explore the underlying principles of these techniques while highlighting the methodological variety. This diversity also implies that arguably many different classification schemes could be devised to group them. Furthermore, it is important to realize that labeling techniques belonging to a single category can be further complicated given that some methods are conceptually hybrid approaches that combine the strengths of their individual constituents while mitigating their disadvantages as much as possible. As such, rather than being an exhaustive overview, this chapter should instead be used as a practical guide in the ever-evolving US tracking literature.

Chapter Contents:

  • 13.1 General principles
  • 13.2 Classification of speckle tracking techniques
  • 13.2.1 Input data type
  • 13.2.2 Data dimensionality
  • 13.2.3 Temporal tracking strategy
  • 13.3 Overview of speckle tracking techniques
  • 13.3.1 Doppler-based methods and 1D motion estimators
  • Time-shift (or time-delay) estimators
  • Phase-shift estimators
  • 13.3.2 Optical flow methods
  • 13.3.3 Registration-based methods
  • 13.3.4 Biomechanical models
  • 13.3.5 Statistical models
  • 13.3.6 Segmentation-based methods
  • 13.4 Determinants of speckle tracking performance
  • 13.4.1 Spatial resolution
  • Point-spread function
  • Sampling criteria
  • High frequency imaging
  • Transverse oscillations beamforming
  • Directional beamforming
  • Other beamforming strategies
  • 13.4.2 Temporal resolution
  • Optimal frame rate
  • Intrinsic frame rate trade-offs
  • Fast imaging sequences
  • 13.4.3 Other factors
  • Out-of-plane motion
  • Image quality
  • Cramer–Rao lower bound
  • Tissue type
  • Algorithm parameter tuning
  • References

Inspec keywords: biomedical ultrasonics; object tracking; medical image processing; biological tissues; image classification; motion estimation

Other keywords: medical ultrasound images; speckle tracking; classification schemes; tissue motion; time series

Subjects: Sonic and ultrasonic radiation (medical uses); Sonic and ultrasonic radiation (biomedical imaging/measurement); Sonic and ultrasonic applications; Image recognition; Biology and medical computing; Computer vision and image processing techniques; Patient diagnostic methods and instrumentation

Preview this chapter:
Zoom in

Principles of speckle tracking, Page 1 of 2

| /docserver/preview/fulltext/books/he/pbhe013e/PBHE013E_ch13-1.gif /docserver/preview/fulltext/books/he/pbhe013e/PBHE013E_ch13-2.gif

Related content

This is a required field
Please enter a valid email address