Tracing the tongue with GLoSsatron

1. 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

2. 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

3. 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

4. Rarely this nice Ultrafest III, University of Arizona

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

6. Graininess Ultrafest III, University of Arizona

7. Beamforming artifacts Ultrafest III, University of Arizona

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

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

10. 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

11. 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.

12. 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

13. 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

14. 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

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

16. 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.

17. 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.

18. 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.

19. 3. Zeroing In • At this point the Sobel (gradient) filter becomes helpful. • 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.

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

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

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

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

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

26. 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.

27. Catching Errors • 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.

29. Catching Errors • 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.

31. 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?

32. Rarely this nice Ultrafest III, University of Arizona

33. Rarely this nice Ultrafest III, University of Arizona

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

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

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

37. Graininess Ultrafest III, University of Arizona

38. Graininess Ultrafest III, University of Arizona

39. Beamforming artifacts Ultrafest III, University of Arizona

40. Beamforming artifacts Ultrafest III, University of Arizona

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

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

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

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

45. 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