diff_classifier.imagej

diff_classifier.imagej

Tools to perform particle tracking with Trackmate ImageJ plugin.

This module includes functions for prepping images and performing tracking using the TrackMate ImageJ plugin [Rb2eabc920836-1].

References

[Rb2eabc920836-1]

Tinevez, JY,; Perry, N. & Schindelin, J. et al. (2016), “TrackMate: an open and extensible platoform for single-particle tracking.”, Methods 115: 80-90, PMID 27713081 (on Google Scholar).

diff_classifier.imagej.mean_intensity(local_im, frame=0)

Calculates mean intensity of first frame of input image.

Parameters:

local_im : string

Location of input image.

frame : int

Frame at which to perform mean.

Returns:

test_intensity : float

Mean intensity of input image.

Examples

>>> mean_intensity('your/sample/image')

diff_classifier.imagej.partition_im(tiffname, irows=4, icols=4, ores=(2048, 2048), ires=(512, 512))

Partitions image into smaller images.

Partitions a large image into irows x icols images of size ires and saves them. Also forces image to be square. Default input image sizes are from a Nikon/Hamamatsu camera setup (2048 x 2044 pixels).

Parameters:

tiffname : string

Location of input image to be partitioned.

irows : int

Number of rows of size ires pixels to be partitioned from source image.

icols : int

Number of columns of size ires pixels to be partitioned from source image.

ores : tuple of int

Input images are scaled to size ores pixels prior to splitting.

ires : tuple of int

Output images are of size ires pixels.

Examples

>>> partition_im('your/sample/image.tif', irows=8, icols=8, ires=(256, 256))

diff_classifier.imagej.regress_sys(folder, all_videos, yfit, training_size, randselect=True, trainingdata=[], frame=0, have_output=True, download=True, bucket_name='ccurtis.data')

Uses regression based on image intensities to select tracking parameters.

This function uses regression methods from the scikit-learn module to predict the lower quality cutoff values for particle filtering in TrackMate based on the intensity distributions of input images. Currently only uses the first frame of videos for analysis, and is limited to predicting quality values.

In practice, users will run regress_sys twice in different modes to build a regression system. First, set have_output to False. Function will return list of randomly selected videos to include in the training dataset. The user should then manually track particles using the Trackmate GUI, and enter these values in during the next round as the input yfit variable.

Parameters:

folder : str

S3 directory containing video files specified in all_videos.

all_videos: list of str

Contains prefixes of video filenames of entire video set to be tracked. Training dataset will be some subset of these videos.

yfit: numpy.ndarray

Contains manually acquired quality levels using Trackmate for the files contained in the training dataset.

training_size : int

Number of files in training dataset.

randselect : bool

If True, will randomly select training videos from all_videos. If False, will use trainingdata as input training dataset.

trainingdata : list of str

Optional manually selected prefixes of video filenames to be used as training dataset.

have_output: bool

If you have already acquired the quality values (yfit) for the training dataset, set to True. If False, it will output the files the user will need to acquire quality values for.

bucket_name : str

S3 bucket containing videos to be downloaded for regression calculations.

Returns:

regress_object : list of sklearn.svm.classes.

Contains list of regression objects assembled from the training datasets. Uses the mean, 10th percentile, 90th percentile, and standard deviation intensities to predict the quality parameter in Trackmate.

tprefix : list of str

Contains randomly selected images from all_videos to be included in training dataset.

diff_classifier.imagej.regress_tracking_params(regress_object, to_track, regmethod='LinearRegression', frame=0)

Predicts quality values to be used in particle tracking.

Uses the regress object from regress_sys to predict tracking parameters for TrackMate analysis.

Parameters:

regress_object: list of sklearn.svm.classes.

Obtained from regress_sys

to_track: string

Prefix of video files to be tracked.

regmethod: {‘LinearRegression’, ‘SVR’, ‘SGDRegressor’, ‘BayesianRidge’,

‘LassoLars’, ‘ARDRegression’, ‘PassiveAggressiveRegressor’, ‘TheilSenRegressor’} Desired regression method.

Returns:

fqual: float

Predicted quality factor used in TrackMate analysis.

diff_classifier.imagej.track(target, out_file, template=None, fiji_bin=None, tparams={'do_median_filtering': False, 'gap_closing_max_distance': 10.0, 'linking_max_distance': 6.0, 'max_frame_gap': 3, 'median_intensity': 300.0, 'quality': 15.0, 'radius': 3.0, 'snr': 0.0, 'threshold': 0.0, 'track_duration': 20.0, 'xdims': (0, 511), 'ydims': (1, 511)})

Performs particle tracking on input video.

Particle tracking is performed with the ImageJ plugin Trackmate. Outputs a csv file containing analysis settings and particle trajectories.

Parameters:

target : str

Full path to a tif file to do tracking on. Can also be a URL (e.g., ‘http://fiji.sc/samples/FakeTracks.tif’)

out_file : str

Full path to a csv file to store the results.

template : str, optional

The full path of a template for tracking. Defaults to use data/trackmate_template.py stored in the diff_classifier source-code.

fiji_bin : str

The full path to ImageJ executable file. Includes default search locations for Mac and Linux systems.

radius : float

Estimated radius of particles in image.

threshold : float

Threshold value for particle detection step.

do_median_filtering : bool

If True, performs a median filter on video prior to tracking.

quality : float

Lower quality cutoff value for particle filtering.

xdims : tuple of int

Upper and lower x limits for particle filtering.

ydims : tuple of int

Upper and lower y limits for particle filtering.

median_intensity : float

Lower median intensity cutoff value for particle filtering.

snr : float

Lower signal to noise ratio cutoff value for particle filtering.

limking_max_distance : float

Maximum allowable distance in pixels between two frames to join particles in track.

gap_closing_max_distance : float

Maximum allowable distance in pixels between more than two frames to join particles in track.

max_frame_gap : int

Maximum allowable number of frames a particle is allowed to leave video and be counted as same trajectory.

track_duration : float

Lower duration cutoff in frames for trajectory filtering.