1. 1 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Cédric Dufour ( LTS-IBCM Collaboration ) The ‘microtubules’ project

2. 2 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne  The ‘microtubules’ project  Morphological filtering  Markers extraction  Microtubules extraction  Results  Algorithm testing  Final assessment  Content

3. 3 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne  Obtain specific proteins’ density statistics, related to the neural cell microtubules structures. Markers The ‘microtubules’ project  The goal Microtubules

4. 4 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne The ‘microtubules’ project  The various steps  Isolate the markers  mask.  Isolate the microtubules  mask and skeleton.  Compute the microtubules’ surface and length.  Compute the markers’ quantity, overall and near the microtubules.

5. 5 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne The ‘microtubules’ project  The way it is done  Markers extraction : Morphological filtering and local maximum detection.  Microtubules extraction : Selective filtering using linear oriented structuring element’s correlation with thresholded image.

6. 6 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Morphological filtering  What is it ?  Morphological filtering is a filtering method originated from the theory of mathematical morphology.  The base of all morphological processing are the ‘erosion’ and ‘dilation’ morphological functions.

7. 7 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Morphological filtering  Binary ‘erosion’ Original setEroded set Structuring elmt.

8. 8 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Morphological filtering  Binary ‘dilation’ Original setDilated set Structuring elmt.

9. 9 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Morphological filtering  Binary ‘opening’ Original setOpened set Structuring elmt.

10. 10 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Morphological filtering  Binary ‘closing’ Original setClosed set Structuring elmt.

11. 11 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne  Replace the intersection/union operators with infimum/supremum operators Morphological filtering  Function (or ‘gray scale’) morphology Original fct. Dilated fct. Eroded fct. Original fct. Closed fct. Opened fct.

12. 12 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Base image Markers extraction  Step by step :1

13. 13 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Opening with a 3x3 square Markers extraction  Step by step :2

14. 14 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Subtraction with original image (Tophat) Markers extraction  Step by step :3

15. 15 Signal Processing Laboratory Swiss Federal Institute of Technology, LausanneNormalization Markers extraction  Step by step :4

16. 16 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne ‘Loosy’ threshold Markers extraction  Step by step :5

17. 17 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Remove small elements (artefacts) Markers extraction  Step by step :6

18. 18 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Mask base image ( and normalize ) Markers extraction  Step by step :7

19. 19 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Local maximum detection ( in 5x5 disc neighborhood ) Markers extraction  Step by step :8

20. 20 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Resulting markers mask Markers extraction  In the end

21. 21 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Base image Microtubules extraction  Step by step :1

22. 22 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Opening with 21x21 square (  background) Microtubules extraction  Step by step :2

23. 23 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Subtraction with original image (Tophat) Microtubules extraction  Step by step :3

24. 24 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Markers removal Microtubules extraction  Step by step :4

25. 25 Signal Processing Laboratory Swiss Federal Institute of Technology, LausanneThreshold Microtubules extraction  Step by step :5

26. 26 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Resulting binary image Microtubules extraction  Step by step :6

27. 27 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Oriented linear element correlation Microtubules extraction  Step by step :7

28. 28 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Small elements (artefacts) removal Microtubules extraction  Step by step :8

29. 29 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Repeat last 2 steps for different orientations Microtubules extraction  Step by step :9

30. 30 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Thresholding of filter accumulator Microtubules extraction  Step by step :10

31. 31 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Closing with a 3x3 cross to remove irregularities Microtubules extraction  Step by step :11

32. 32 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Skeleton by thinning and cntd. points suppressing Microtubules extraction  Step by step :12

33. 33 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Resulting microtubules mask and skeleton Microtubules extraction  In the end

34. 34 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Results  Image 1 (demo image)

35. 35 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Results  Image 2 (low density)

36. 36 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Results  Image 3 (high density)

37. 37 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Errors  markers: 0% / 4.8% Algorithm testing  Real image (PSNR = 35dB)

38. 38 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Errors  microtubules: 0.7% / 0.5%; markers: 0% / 0% Algorithm testing  Synthetic image (PSNR = 35dB)

39. 39 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne  We’ve been able to offer the biologists… – a fully automatic analysis program, – running in a powerful and wide-spread environment (MatLab  ), – giving good results, according to the biologists’ needs. Final assessment  In general

40. 40 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne  Microtubules mask extraction : The poor quality of the input images (very low contrast) leads to a low-efficiency microtubules mask extraction procedure (the algorithm misses the most evanescent structures). Final assessment  Problems

41. 41 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne  Microtubules mask extraction : Use tracking algorithm to follow the full microtubule (pseudo-linear) structure. N.B. This is not easy because of the variable number of microtubules that may cross in one point (resulting in tracking uncertainties) Final assessment  Improvements

42. 42 Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne Thank you for your attention