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Getting started with TrackMate

13 bytes removed, 10:52, 31 July 2013
Choosing a segmenter
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== Choosing a segmenter detector ==
[[Image:TrackMate SegmenterChoice.png|right|border|]]
You are now offered to choose a segmentation detection algorithm ("segmenterdetector") amongst the currently implemented ones.
The choice is actually quite limited. Apart from the '''Manual segmentationannotation''', you will find 3 segmentersdetectors, but they are all based on [http://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian LoG (Laplacian of Gaussian) segmentation]. They are described in detail elsewhere, but here is what you need to know.
* The '''Log segmenterdetector''' applies a plain LoG segmentation on the image. All calculations are made in the Fourier space, which makes it optimal for intermediate spot sizes, between ≈5 and ≈20 pixels in diameter.* The '''Dog segmenterdetector''' uses the [http://en.wikipedia.org/wiki/Blob_detection#The_difference_of_Gaussians_approach difference of Gaussians] approach to approximate a LoG filter by the difference of 2 Gaussians. Calculations are made in the direct space, and it is optimal for small spot sizes, below ≈5 pixels.* The '''Downsmapled LoG segmenterdetector''' uses the LoG segmenterdetector, but downsizes the image by an integer factor before filtering. This makes it optimal for large spot sizes, above ≈20 pixels in diameter, at the cost of localization precision.
In our case, let us just use the '''Dog segmenterdetector'''.
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