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Getting started with TrackMate

292 bytes removed, 06:01, 21 October 2015
The detector configuration panel: Fix incorrect statement: the threshold operates on QUALITY, not on maximal intensity. Noticed by deepika.sharma@unibas.ch
The LoG-based detectors fortunately demand very few parameters to tune them. The only really important one is the ''Estimated blob diameter'''. Just enter the approximate size of the spots you are looking to tracks. Careful: you are expected to enter it in <u>physical units</u>. In our dummy example, there is no calibration (<tt>1 pixel = 1 pixel</tt>), so it does not appear here.
There are extra fields that you can configure also. The '''Threshold''' numerical value aims at helping dealing with situation where a gigantic number of spots can be found. Every spot with a maximal intensity quality value below this threshold value will not be retained, which can help saving memory. You set this field manually, or by adjusting the threshold using ImageJ: call the {{bc | Image | Adjust | Threshold}} menu item ({{key|Ctrl}}+{{key|Shift}}+{{key|T}}), adjust the upper threshold to your liking, the press and check how it fares with the '''Refresh thresholdPreview button''' button on the panel. This will grab the value you just set.
You can check '''Use median filter''': this will apply a 3x3 median filter prior to any processing. This can help dealing with images that have a marked salt & pepper noise which generates spurious spots.
We hope that TrackMate will be used in experiments requiring '''Sub-pixel localization''', such as following motor proteins in biophysical experiments, so we added schemes to achieve this. The one currently implemented uses a quadratic fitting scheme (made by Stephan Saalfeld and Stephan Preibisch) based on [http://www.cs.ubc.ca/~lowe/keypoints/ David Lowe SIFT work]<ref>David G. Lowe, "Distinctive image features from scale-invariant keypoints", International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.</ref>. It has the advantage of being very quick, compared to the segmentation time itself.
Finally, there is a '''Preview button''' that allows to quickly check your parameters on the current data. To use it, just draw a ROI in the raw image, and click preview. After some computations, you can check the results overlaid on the image. Most likely, you will see plenty of spurious spots that you will be tempted to discard by adjusting the '''Threshold''' value. This is a very good approach for large problems. Here, we care little for that, just leave the threshold at 0.
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