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Optical Flow For Live Cell Imaging PDF Print
Article Index
Optical Flow For Live Cell Imaging
Optical Flow Methods
Ground Truth Image Series Generator
Motion Tracking Application
Related Publications

Motion Tracking Application

We are half way to a motion tracker now. We have already implemented simple version of it. Basically, points of interest are selected in the first frame of the sequence. Their coordinates of every point is adjusted into the next frame with its movement based only on the flow information found at the particular coordinate. No additional heuristics is conducted. Here, we will present our first results on the artificially generated data.

Inputs:

Gray level image sequence ad_seq3_001_tn.png
Positions of objects of interests 3out_001_tn.png

Algorithm:

  • Compute the optic flow between consecutive frame pairs. For our data, we used the most suitable method, with respect to Average angular error, with best parameter settings (see chapter Related Publications).
3out_001_rgb.png gtflow.png color.png
Computed flow Ground truth flow

Legend(color codes flow direction, intensity vector length)

  • Track selected objects of interest using computed flow starting from their initial positions. The tracking is performed as follows. We use a flow computed between the first and the second frame. We consider the vector at the position of an examined object. We simply add this vector to the object coordinate. This way we get the position of examined object in the second frame. This is also an initial position for the processing of the movements between the second and the third frame. The process is repeated until the last frame.
    3out_001_tn.png
    3out_001_tn.png
    Computed tracking Ground truth tracking

Outputs:

The application saves the positions of each object in each frame. It also computes some basic statistics. The movement maps are saved.

comp_crosses_tn.png
gt_crosses_tn.png
Computed movement map Ground truth movement map

The described prototype of tracking application is very simple. Surprisingly, it works really good and therefore we are motivated to develop more sophisticated version of it. We hope that if we take into account interactions between objects and add some image procesing into the tracking application, we can get even better results than we have at the moment. It should be emphasized that all examples on this page are for 2D sequences. Nevertheless, our framework is able to process even 3D time-lapse image sequences.

Finally, we present tracking results for two real-world live-cell image sequences. Tracking of HP2010 protein domains in HL-60 cells is in the first example. Tracking of telomers is in the second example.

2out_001.png 2out_001.png
Tracking of HP2010 domains in HL-60 cells
Tracking of telomers

 

The tracker is available under GNU GPL as part of the OpticalFlow collection as a tool of_tracker.download tracker



Written by Vladimír Ulman   
Last Updated ( Wednesday, 19 September 2012 )
 
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