Tracing the tongue with glossatron
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Tracing the tongue with GLoSsatron. Adam Baker, Jeff Mielke, Diana Archangeli University of Arizona Supported by College of Social and Behavioral Sciences, University of Arizona James S. McDonnell Foundation #220020045 BBMB. The Need.

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Tracing the tongue with glossatron

Tracing the tongue with GLoSsatron

Adam Baker, Jeff Mielke, Diana Archangeli

University of Arizona

Supported by

College of Social and Behavioral Sciences, University of Arizona

James S. McDonnell Foundation #220020045 BBMB

Ultrafest III, University of Arizona


The need

The Need

  • Taking point measurements from ultrasound images is tedious and time-consuming.

    • even when simple methods are used

    • easily 75% of the time required to run an experiment

  • Obtaining measurements automatically would ameliorate that problem.

Ultrafest III, University of Arizona


The problem

The Problem

  • There are a number of features that make ultrasound images difficult to measure automatically.

  • A tour of the problem…

Ultrafest III, University of Arizona


Rarely this nice

Rarely this nice

Ultrafest III, University of Arizona


Potentially ill formed lines

Potentially Ill-formed Lines

?

Ultrafest III, University of Arizona


Graininess

Graininess

Ultrafest III, University of Arizona


Beamforming artifacts

Beamforming artifacts

Ultrafest III, University of Arizona


Variable illumination

Variable “illumination”

Ultrafest III, University of Arizona


Phantom palates

“Phantom palates”

Really an ultrasound artifact

Ultrafest III, University of Arizona


Technology vs biology

Technology vs. Biology

  • Problems are attributable to

    • ultrasound technology

    • speaker idiosyncrasies

      • hydration level that day

      • muscle morphology

      • pressure applied to transducer

      • waddle (good)

      • scruff (bad)

Ultrafest III, University of Arizona


Technology vs biology1

Technology vs. Biology

  • The magnitude of the problem can be reduced considerably if we have high standards for our subjects.

  • This is a more practical solution for studies of English speakers than for work in other languages.

  • I suggest that a goal of automatic edge detection should be an algorithm that works (fairly well) for non-ideal images.


Glossatron

GLoSsatron

  • GLoSsatron is a system intended to produce quality surfaces

    • for a wide range of image qualities

    • with a minimum of input from the experimenter

Ultrafest III, University of Arizona


Glossatron1

GLoSsatron

  • It is named for the three filters used to enhance the tongue surface.

    • Gaussian

    • Laplacian

    • Sobel

  • Why are filters needed at all?

Ultrafest III, University of Arizona


Too many edges

Too many edges

  • Sobel filter finds the gradient of the image

  • i.e. parts where there’s a change from light to dark

  • Almost useless in such a high noise situation


1 reducing noise

1. Reducing noise

  • A Gaussian convolution is used to eliminate noise.

  • Every pixel is

    replaced by

    a weighted sum

    of itself and its

    neighbors.


2 reducing noise

2. Reducing noise

  • The tongue

    surface becomes

    more prominent with respect to the noise in the image.

  • This is equivalent to a low-pass filter.


2 enhancing the edge

2. Enhancing the Edge

  • A Laplacian filter is used to enhance the

    remaining edges

  • The process

    of convolution

    is identical.

  • This is the 2nd

    derivative of the

    Gaussian.


2 enhancing the edge1

2. Enhancing the Edge

  • The tongue surface is now quite prominent w.r.t the rest of the image.

  • The task now is to identify the tongue surface.


3 zeroing in

3. Zeroing In

  • At this point the Sobel (gradient) filter becomes helpful.

  • The tongue surface is now quite prominent.


Searching for the surface

Searching for the surface

  • To find the surface we use a radial grid, we search along predefined radii.


Searching along a radius

Searching Along a Radius

  • Search in a user-defined portion of the radius.

Ultrafest III, University of Arizona


Searching along a radius1

Searching Along a Radius

  • Find the maximum point of the Laplacian

Ultrafest III, University of Arizona


Searching along a radius2

Searching Along a Radius

  • Find the corresponding point on the Sobel.

Ultrafest III, University of Arizona


Searching along a radius3

Searching Along a Radius

  • Find the first lower maximum on the Sobel.

Ultrafest III, University of Arizona


Searching along a radius4

Searching Along a Radius

  • This is the point we want.

Ultrafest III, University of Arizona


Searching for the surface1

Searching for the surface

  • This heuristic is quite simple.

  • A more sophisticated technique will almost certainly yield superior results.

  • However, much is to be gained in post-processing.


Catching errors

Catching Errors

  • No edge detection system will score 100%

Small Gaps

No tongue

to find


Catching errors1

Catching Errors

  • This algorithm misses three real points, and falsely identifies many non-tongue points.


Catching errors2

Catching Errors

  • These are outliers relative to their neighbors; this can be quantified.


Catching errors3

Catching Errors

  • They can be detected and eliminated, either with simple or complex means.


Catching errors4

Catching Errors

  • Experience so far: eliminating false data points is the easiest and most rewarding way to increase the edge detection accuracy.

  • So how about those bad images?


Rarely this nice1

Rarely this nice

Ultrafest III, University of Arizona


Rarely this nice2

Rarely this nice

Ultrafest III, University of Arizona


Potentially ill formed lines1

Potentially Ill-formed Lines

Ultrafest III, University of Arizona


Potentially ill formed lines2

Potentially Ill-formed Lines

?

Ultrafest III, University of Arizona


Potentially ill formed lines3

Potentially Ill-formed Lines

Ultrafest III, University of Arizona


Graininess1

Graininess

Ultrafest III, University of Arizona


Graininess2

Graininess

Ultrafest III, University of Arizona


Beamforming artifacts1

Beamforming artifacts

Ultrafest III, University of Arizona


Beamforming artifacts2

Beamforming artifacts

Ultrafest III, University of Arizona


Variable illumination1

Variable “illumination”

Ultrafest III, University of Arizona


Variable illumination2

Variable “illumination”

Ultrafest III, University of Arizona


Phantom palates1

“Phantom palates”

Really an ultrasound artifact

Ultrafest III, University of Arizona


Phantom palates2

“Phantom palates”

Ultrafest III, University of Arizona


Conclusion

Conclusion

  • GLoSsatron is a new algorithm that can be efficiently implemented for users.

  • The experimenter will supply only a subject-specific search window (i.e. where the tongue is going to appear).

  • This program, as with others like it, has the potential to save experimenters tremendous quantities of time.

Ultrafest III, University of Arizona


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