Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Tracing the tongue Tracing the tongue with GLoSsatronwith GLoSsatron

Adam Baker, Jeff Mielke, Diana Adam Baker, Jeff Mielke, Diana ArchangeliArchangeli

University of ArizonaUniversity of Arizona

Supported by Supported by

College of Social and Behavioral Sciences, University of ArizonaCollege of Social and Behavioral Sciences, University of Arizona

James S. McDonnell Foundation #220020045 BBMB James S. McDonnell Foundation #220020045 BBMB

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

The NeedThe Need

Taking point measurements from Taking point measurements from ultrasound images is tedious and time-ultrasound images is tedious and time-consuming.consuming. even when simple methods are usedeven when simple methods are used easily 75% of the time required to run an easily 75% of the time required to run an

experimentexperiment Obtaining measurements automatically Obtaining measurements automatically

would ameliorate that problem.would ameliorate that problem.

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

The ProblemThe Problem

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

A tour of the problem…A tour of the problem…

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Rarely this niceRarely this nice

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Potentially Ill-formed LinesPotentially Ill-formed Lines

?

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

GraininessGraininess

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Beamforming artifactsBeamforming artifacts

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Variable “illumination”Variable “illumination”

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

““Phantom palates”Phantom palates”

Really an ultrasound artifact

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Technology vs. BiologyTechnology vs. Biology

Problems are attributable toProblems are attributable to ultrasound technologyultrasound technology speaker idiosyncrasiesspeaker idiosyncrasies

hydration level that dayhydration level that day muscle morphologymuscle morphology pressure applied to transducerpressure applied to transducer waddle (good)waddle (good) scruff (bad)scruff (bad)

Technology vs. BiologyTechnology vs. Biology

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

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

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

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

GLoSsatronGLoSsatron

GLoSsatron is a system intended to GLoSsatron is a system intended to produce quality surfacesproduce quality surfaces for a wide range of image qualitiesfor a wide range of image qualities with a minimum of input from the with a minimum of input from the

experimenterexperimenter

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

GLoSsatronGLoSsatron

It is named for the three filters used It is named for the three filters used to enhance the tongue surface.to enhance the tongue surface. GaussianGaussian LaplacianLaplacian SobelSobel

Why are filters needed at all?Why are filters needed at all?

Too many edgesToo many edges

Sobel filter finds Sobel filter finds the gradient of the gradient of the imagethe image

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

Almost useless in Almost useless in such a high noise such a high noise situationsituation

1. Reducing noise1. Reducing noise

A Gaussian convolution is used to A Gaussian convolution is used to eliminate noise.eliminate noise.

Every pixel isEvery pixel is

replaced byreplaced by

a weighted suma weighted sum

of itself and itsof itself and its

neighbors.neighbors.

2. Reducing noise2. Reducing noise

The tongueThe tongue

surface becomessurface becomes

more prominent more prominent with respect to with respect to the noise in the the noise in the image.image. This is This is equivalent to a equivalent to a low-pass filter.low-pass filter.

2. Enhancing the Edge2. Enhancing the Edge

A Laplacian filter is used to enhance theA Laplacian filter is used to enhance the

remaining edgesremaining edges The processThe process

of convolutionof convolution

is identical.is identical. This is the 2This is the 2ndnd

derivative of thederivative of the

Gaussian.Gaussian.

2. Enhancing the Edge2. Enhancing the Edge

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

The task now is The task now is to identify the to identify the tongue surface.tongue surface.

3. Zeroing In3. Zeroing In

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

The tongue The tongue surface is now surface is now quite prominent.quite prominent.

Searching for the surfaceSearching for the surface

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

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Searching Along a RadiusSearching Along a Radius

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

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Searching Along a RadiusSearching Along a Radius

Find the maximum point of the Find the maximum point of the LaplacianLaplacian

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Searching Along a RadiusSearching Along a Radius

Find the corresponding point on the Find the corresponding point on the Sobel.Sobel.

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Searching Along a RadiusSearching Along a Radius

Find the first lower maximum on the Find the first lower maximum on the Sobel.Sobel.

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Searching Along a RadiusSearching Along a Radius

This is the point we want.This is the point we want.

Searching for the surfaceSearching for the surface

This heuristic is quite simple.This heuristic is quite simple. A more sophisticated technique will A more sophisticated technique will almost certainly yield superior results.almost certainly yield superior results.

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

Catching ErrorsCatching Errors

No edge detection system will score No edge detection system will score 100%100%

Small GapsNo tongue to find

Catching ErrorsCatching Errors

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

Catching ErrorsCatching Errors

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

Catching ErrorsCatching Errors

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

Catching ErrorsCatching Errors

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

So how about those bad images?So how about those bad images?

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Rarely this niceRarely this nice

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Rarely this niceRarely this nice

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Potentially Ill-formed LinesPotentially Ill-formed Lines

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Potentially Ill-formed LinesPotentially Ill-formed Lines

?

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Potentially Ill-formed LinesPotentially Ill-formed Lines

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

GraininessGraininess

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

GraininessGraininess

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Beamforming artifactsBeamforming artifacts

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Beamforming artifactsBeamforming artifacts

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Variable “illumination”Variable “illumination”

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

Variable “illumination”Variable “illumination”

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

““Phantom palates”Phantom palates”

Really an ultrasound artifact

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

““Phantom palates”Phantom palates”

Ultrafest III, University of ArizonaUltrafest III, University of Arizona

ConclusionConclusion

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

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

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