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Cédric Dufour ( LTS-IBCM Collaboration )

Cédric Dufour ( LTS-IBCM Collaboration ). The ‘ microtubules ’ project.  Content. The ‘ microtubules ’ project Morphological filtering Markers extraction Microtubules extraction Results Algorithm testing Final assessment. Microtubules. Markers.

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Cédric Dufour ( LTS-IBCM Collaboration )

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  1. Cédric Dufour( LTS-IBCM Collaboration ) The ‘microtubules’ project

  2. Content • The ‘microtubules’ project • Morphological filtering • Markers extraction • Microtubules extraction • Results • Algorithm testing • Final assessment

  3. Microtubules Markers The ‘microtubules’ projectThe goal • Obtain specific proteins’ density statistics, related to the neural cell microtubules structures.

  4. 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. 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. 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. Original set Eroded set Structuring elmt. Morphological filtering  Binary ‘erosion’

  8. Original set Dilated set Structuring elmt. Morphological filtering  Binary ‘dilation’

  9. Original set Opened set Structuring elmt. Morphological filtering  Binary ‘opening’

  10. Original set Closed set Structuring elmt. Morphological filtering  Binary ‘closing’

  11. Original fct. Dilated fct. Closed fct. Original fct. Eroded fct. Opened fct. Morphological filtering  Function (or ‘gray scale’) morphology • Replace the intersection/union operators with infimum/supremum operators

  12. Markers extraction  Step by step : 1 Base image

  13. Markers extraction  Step by step : 2 Opening with a 3x3 square

  14. Markers extraction  Step by step : 3 Subtraction with original image (Tophat)

  15. Markers extraction  Step by step : 4 Normalization

  16. Markers extraction  Step by step : 5 ‘Loosy’ threshold

  17. Markers extraction  Step by step : 6 Remove small elements (artefacts)

  18. Markers extraction  Step by step : 7 Mask base image ( and normalize )

  19. Markers extraction  Step by step : 8 Local maximum detection ( in 5x5 disc neighborhood )

  20. Markers extraction  In the end Resulting markers mask

  21. Microtubules extraction  Step by step : 1 Base image

  22. Microtubules extraction  Step by step : 2 Opening with 21x21 square ( background)

  23. Microtubules extraction  Step by step : 3 Subtraction with original image (Tophat)

  24. Microtubules extraction  Step by step : 4 Markers removal

  25. Microtubules extraction  Step by step : 5 Threshold

  26. Microtubules extraction  Step by step : 6 Resulting binary image

  27. Microtubules extraction  Step by step : 7 Oriented linear element correlation

  28. Microtubules extraction  Step by step : 8 Small elements (artefacts) removal

  29. Microtubules extraction  Step by step : 9 Repeat last 2 steps for different orientations

  30. Microtubules extraction  Step by step : 10 Thresholding of filter accumulator

  31. Microtubules extraction  Step by step : 11 Closing with a 3x3 cross to remove irregularities

  32. Microtubules extraction  Step by step : 12 Skeleton by thinning and cntd. points suppressing

  33. Microtubules extraction  In the end Resulting microtubules mask and skeleton

  34. Results  Image 1 (demo image)

  35. Results  Image 2 (low density)

  36. Results  Image 3 (high density)

  37. Algorithm testing  Real image (PSNR = 35dB) Errors  markers: 0% / 4.8%

  38. Algorithm testing  Synthetic image (PSNR = 35dB) Errors  microtubules: 0.7% / 0.5%; markers: 0% / 0%

  39. Final assessment In general • 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.

  40. Final assessment Problems • 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).

  41. Final assessment Improvements • 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)

  42. Thank you for your attention

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