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IDL 102 (Particle Tracking)

IDL 102 (Particle Tracking). Our MO. Convert Image DV -> gdf Do not byte-scale Also, do byte scale (after conversion) Find dots in each frame “Clean” image Find all candidate dots Refine dots set Link dots frame-to-frame to get trajectories How far are we willing to look for a particle?

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IDL 102 (Particle Tracking)

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  1. IDL 102 (Particle Tracking)

  2. Our MO • Convert Image DV -> gdf • Do not byte-scale • Also, do byte scale (after conversion) • Find dots in each frame • “Clean” image • Find all candidate dots • Refine dots set • Link dots frame-to-frame to get trajectories • How far are we willing to look for a particle? • How about gaps? • How about blinky, random noise?

  3. Conversion isn’t futile • Images are acquired at 16bit (0 -> 216=65536) • Images have to be displayed at 8bit (0 -> 28=256)

  4. Conversion isn’t futile • Images are acquired at 16bit (0 -> 216=65536) • Images have to be displayed at 8bit (0 -> 28=256)

  5. Conversion isn’t futile • Images are acquired at 16bit (0 -> 216=65536) • Images have to be displayed at 8bit (0 -> 28=256)

  6. HOT pixels are bad

  7. HOT pixels are bad

  8. DEAD pixels are bad too

  9. DEAD pixels are bad too

  10. Pre-tracking • Find dots in each frame • “Clean” image • Find all candidate dots • Refine dots set

  11. Cleaning the image: band-passing with bpass.pro

  12. Cleaning the image: band-passing with bpass.pro

  13. Cleaning the image: band-passing with bpass.pro

  14. Cleaning the image: band-passing with bpass.pro

  15. Cleaning the image: band-passing with bpass.pro

  16. Cleaning the image: band-passing with bpass.pro

  17. These could be candidates

  18. Select them with a reasonable masscut

  19. You need to refine them using reasonable criteria X Y Mass Radius Eccentricity Time 0 1 2 3 4 5 0 3 4 5 6 7 0 1 8 9 10 11 12 0 2 13 14 15 16 17 0 3 18 19 20 21 22 0 4 23 24 25 26 27 2 5 28 29 30 31 32 2 6 33 34 35 36 37 3 7 38 39 40 41 42 4 8 43 44 45 46 47 5 9 48 49 50 51 52 5 10 53 54 55 56 57 5 11 58 59 60 61 62 5 12 63 64 65 66 67 6 13 68 69 70 71 72 12 14 73 74 75 76 77 12

  20. Criteria • X, Y can be used to clip edges, where things usually go wrong • “Mass” is total integrated brightness in each blob • Radius of gyration is a measure of size that makes dimmer pixels count less • Eccentricity: • 0: a perfect disk • 1: a perfect line segment • Use eclip.pro, where(), or edgeclip.pro to refine ranges of these criteria

  21. A few things you can use to evaluate your pretracking: • Say your pretracked data is in ‘pt’ • Overview: • ptexplore, pt • Number of particles at each frame (bleaching?) • plot_hist, pt[5,*], bin=1 • Bias towards integer pixel positions • plot_hist, pt[0:1,*] mod 1, bin=.05

  22. Now track them! • User defined params for tracking: • Distance to look for same particle frame-to-frame • This must me less than interparticle distance in each frame • Number of frames a particle is allowed to disappear • This must be less than time it takes for two particles to switch position • Shortest trajectory you consider real • This is a toughie. But setting this to something >0 helps get rid of artifacts that blink

  23. Check tracking with • P(dx, dt=1)

  24. Check tracking with • P(dx, dt=1)

  25. Check tracking with • P(dx, dt=1)

  26. Check with IDover2D

  27. Analysis

  28. AnalyzeMean squared displacement (MSD)

  29. AnalyzeMean squared displacement (MSD) 7 4 9 11 2 t=1 13 10 6 12 5 15 14 3 8

  30. AnalyzeMean squared displacement (MSD) 7 4 9 11 2 t=1 13 10 6 12 5 15 14 3 8 • Measure all displacements that are Dt = 1 frame apart • Square them • Average them • Average over other particles if desired and “allowed” MSD(mm2) 1 13 Dt (frames)

  31. AnalyzeMean squared displacement (MSD) 7 4 9 11 2 t=1 13 10 6 12 5 15 14 3 8 • Measure all displacements that are Dt = 1 frame apart • Square them • Average them • Average over other particles if desired and “allowed” MSD(mm2) • Then do it for Dt = 2 framesand so on 1 2 13 Dt (frames)

  32. Says Einstein! constant in biology y = m x x m= D1 m= D2 MSD(mm2) Dt (frames)

  33. A Yes/No test for diffusion(What’s this about logs and slopes of 1?)

  34. A Yes/No test for diffusion(What’s this about logs and slopes of 1?) 1 1 y = c + m x x 2

  35. Ballistic motion(that of projectiles) Constant velocity gives a slope 2 on a log-log plot

  36. What about this mess of an MSD plot?

  37. What about this mess of an MSD plot?

  38. What about this mess of an MSD plot?

  39. What about this mess of an MSD plot?

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