1. Ultrafest III, University of Arizona 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

2. Ultrafest III, University of Arizona The Need Taking point measurements from ultrasound images is tedious and time- consuming. Taking point measurements from ultrasound images is tedious and time- consuming. even when simple methods are used even when simple methods are used easily 75% of the time required to run an experiment easily 75% of the time required to run an experiment Obtaining measurements automatically would ameliorate that problem. Obtaining measurements automatically would ameliorate that problem.

3. Ultrafest III, University of Arizona The Problem There are a number of features that make ultrasound images difficult to measure automatically. There are a number of features that make ultrasound images difficult to measure automatically. A tour of the problem… A tour of the problem…

4. Ultrafest III, University of Arizona Rarely this nice

5. Ultrafest III, University of Arizona Potentially Ill-formed Lines ?

6. Ultrafest III, University of Arizona Graininess

7. Beamforming artifacts

8. Ultrafest III, University of Arizona Variable “illumination”

9. Ultrafest III, University of Arizona “Phantom palates” Really an ultrasound artifact

10. Ultrafest III, University of Arizona Technology vs. Biology Problems are attributable to Problems are attributable to ultrasound technology ultrasound technology speaker idiosyncrasies speaker idiosyncrasies hydration level that day hydration level that day muscle morphology muscle morphology pressure applied to transducer pressure applied to transducer waddle (good) waddle (good) scruff (bad) scruff (bad)

11. Technology vs. Biology The magnitude of the problem can be reduced considerably if we have high standards for our subjects. 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. 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. I suggest that a goal of automatic edge detection should be an algorithm that works (fairly well) for non-ideal images.

12. Ultrafest III, University of Arizona GLoSsatron GLoSsatron is a system intended to produce quality surfaces GLoSsatron is a system intended to produce quality surfaces for a wide range of image qualities for a wide range of image qualities with a minimum of input from the experimenter with a minimum of input from the experimenter

13. Ultrafest III, University of Arizona GLoSsatron It is named for the three filters used to enhance the tongue surface. It is named for the three filters used to enhance the tongue surface. Gaussian Gaussian Laplacian Laplacian Sobel Sobel Why are filters needed at all? Why are filters needed at all?

14. Too many edges Sobel filter finds the gradient of the image Sobel filter finds the gradient of the image i.e. parts where there’s a change from light to dark i.e. parts where there’s a change from light to dark Almost useless in such a high noise situation Almost useless in such a high noise situation

15. 1. Reducing noise A Gaussian convolution is used to eliminate noise. A Gaussian convolution is used to eliminate noise. Every pixel is Every pixel is replaced by a weighted sum of itself and its neighbors.

16. 2. Reducing noise The tongue The tongue surface becomes more prominent with respect to the noise in the image. This is equivalent to a low-pass filter. This is equivalent to a low-pass filter.

17. 2. Enhancing the Edge A Laplacian filter is used to enhance the A Laplacian filter is used to enhance the remaining edges The process The process of convolution is identical. This is the 2 nd This is the 2 nd derivative of the Gaussian.

18. 2. Enhancing the Edge The tongue surface is now quite prominent w.r.t the rest of the image. The tongue surface is now quite prominent w.r.t the rest of the image. The task now is to identify the tongue surface. The task now is to identify the tongue surface.

19. 3. Zeroing In At this point the Sobel (gradient) filter becomes helpful. At this point the Sobel (gradient) filter becomes helpful. The tongue surface is now quite prominent. The tongue surface is now quite prominent.

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

21. Ultrafest III, University of Arizona Searching Along a Radius Search in a user-defined portion of the radius. Search in a user-defined portion of the radius.

22. Ultrafest III, University of Arizona Searching Along a Radius Find the maximum point of the Laplacian Find the maximum point of the Laplacian

23. Ultrafest III, University of Arizona Searching Along a Radius Find the corresponding point on the Sobel. Find the corresponding point on the Sobel.

24. Ultrafest III, University of Arizona Searching Along a Radius Find the first lower maximum on the Sobel. Find the first lower maximum on the Sobel.

25. Ultrafest III, University of Arizona Searching Along a Radius This is the point we want. This is the point we want.

26. Searching for the surface This heuristic is quite simple. This heuristic is quite simple. A more sophisticated technique will almost certainly yield superior results. A more sophisticated technique will almost certainly yield superior results. However, much is to be gained in post- processing. However, much is to be gained in post- processing.

27. Catching Errors No edge detection system will score 100% No edge detection system will score 100% Small Gaps No tongue to find

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

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

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

31. Catching Errors Experience so far: eliminating false data points is the easiest and most rewarding way to increase the edge detection accuracy. 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? So how about those bad images?

32. Ultrafest III, University of Arizona Rarely this nice

33. Ultrafest III, University of Arizona Rarely this nice

34. Ultrafest III, University of Arizona Potentially Ill-formed Lines

35. Ultrafest III, University of Arizona Potentially Ill-formed Lines ?

36. Ultrafest III, University of Arizona Potentially Ill-formed Lines

37. Ultrafest III, University of Arizona Graininess

38. Graininess

39. Beamforming artifacts

40. Ultrafest III, University of Arizona Beamforming artifacts

41. Ultrafest III, University of Arizona Variable “illumination”

42. Ultrafest III, University of Arizona Variable “illumination”

43. Ultrafest III, University of Arizona “Phantom palates” Really an ultrasound artifact

44. Ultrafest III, University of Arizona “Phantom palates”

45. Ultrafest III, University of Arizona Conclusion GLoSsatron is a new algorithm that can be efficiently implemented for users. 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). 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. This program, as with others like it, has the potential to save experimenters tremendous quantities of time.