IDL 102 Particle Tracking Our MO Convert Image

Our MO • Convert Image DV -> gdf – Do not byte-scale – Also,

Conversion isn’t futile • Images are acquired at 16 bit (0 -> 216=65536) •

Pre-tracking • Find dots in each frame – “Clean” image – Find all candidate

Cleaning the image: band-passing with bpass. pro

You need to refine them using reasonable criteria X 0 1 2 3 4

Criteria • X, Y can be used to clip edges, where things usually go

A few things you can use to evaluate your pretracking: • Say your pretracked

Now track them! • User defined params for tracking: – Distance to look for

Analyze Mean squared displacement (MSD) 4 2 t=1 7 9 13 10 6 5

Analyze Mean squared displacement (MSD) 2 t=1 7 9 13 10 6 5 3

Says Einstein! constant in biology y = MSD(mm 2) mx x m= D 1

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

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

Slides: 45

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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? – How about gaps? – How about blinky, random noise?

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

HOT pixels are bad

DEAD pixels are bad too

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

Cleaning the image: band-passing with bpass. pro

These could be candidates

Select them with a reasonable masscut

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

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

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

Now track them! • User defined params for tracking: – Distance to look for same particle frame-toframe • 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

Check tracking with • P(dx, dt=1)

Check with IDover 2 D

Analysis

Analyze Mean squared displacement (MSD)

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

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

Says Einstein! constant in biology y = MSD(mm 2) mx x m= D 1 m= D 2 Dt (frames)

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

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

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

What about this mess of an MSD plot?