-
-
- Filtering movies with normalizeMovie -
- Why conduct spatial filtering? +
- Filtering movies with normalizeMovie +
- Images from unit test
- Main filtering functions. @@ -373,6 +373,8 @@
Movie Filtering¶
This page documents different features and functions in the CIAtah repository variable for filtering (spatial high/low/bandpass) movies to remove neuropil, cells, or other features.
+
Why conduct spatial filtering?¶
+Spatial filtering can have a large impact on the resulting cell activity traces extracted from the movies and can lead to erroneous conclusions if not properly applied during pre-processing.
+For example, below are the correlations between all cell-extraction outputs from PCA-ICA, ROI back-application of ICA filters, and CNMF-e on a miniature microscope one-photon movie. As can be seen, especially in the case of ROI analysis, the correlation between the activity traces is rendered artificially high due to the correlated background noise. This is greatly reduced in many instances after proper spatial filtering.
+
+ +
Filtering movies with
normalizeMovie¶Users can quickly filter movies using the
normalizeMoviefunction. See below for usage.% Load the movie (be in TIF, HDF5, AVI, etc.). Change HDF5 input dataset name as needed. @@ -2809,34 +2817,32 @@If users set
options.showImages = 0;, thennormalizeMoviewill update a figure containing both real and frequency space before and after the filter has been applied along with an example of the filter in frequency space. This allows users to get a sense of what their filter is doing.
-
Why conduct spatial filtering?¶
-Spatial filtering can have a large impact on the resulting cell activity traces extracted from the movies.
-For example, below are the correlations between all cell-extraction outputs from PCA-ICA, ROI back-application of ICA filters, and CNMF-e on a miniature microscope one-photon movie. As can be seen, especially in the case of ROI analysis, the correlation between the activity traces is rendered artificially high due to the correlated background noise. This is greatly reduced in many instances after proper spatial filtering.
-
- -Images from unit test¶
Main filtering functions.¶
Below is a screen grab from a random frame using all the filtering functions. A nice way to quickly see the many differences between each functions filtering.
-
+ +
Test function filtering¶
-This function will take a movie and This is currently only for the Matlab fft, but I'll see about expanding to others.
-unitNormalizeMovie; +This function will take a movie and conduct multiple spatial filtering operations on it then display for the user.
+ +-ciapkg.unit.unitNormalizeMovie;
+
After running that function, below is an example movie from a prefrontal cortex animal (miniature microscope, GCaMP) showing the difference in results with different spatial filtering.
+Matlab test function¶
--
-
- I've also added the ability to test the parameter space of the Matlab fft, use the below command. -
I've also added the ability to test the parameter space of the Matlab fft, use the below command.
-testMovieFFT = normalizeMovie(testMovie,'normalizationType','matlabFFT_test','secondaryNormalizationType','lowpassFFTDivisive','bandpassMask','gaussian','bandpassType','lowpass');-
-
- Should get a movie output similar to the below, where there is the original movie, the FFT movie, the original/FFT movie, and the dfof of original/FFT movie. -
Should get a movie output similar to the below, where there is the original movie, the FFT movie, the original/FFT movie, and the dfof of original/FFT movie.
+
This can also be expanded to look at the effects of different spatial frequency filters on the resulting output, as indicated below.
+Matlab test function movie output¶
Similar to above, showing results when using
-lowpassFFTDivisivenormalization (using thematlab divide by lowpass before registeringoption inmodelPreprocessMovieandviewMovieRegistrationTestfunctions) withfreqLow = 0andfreqHighset to1,4, and20. This corresponds to removing increasingly smaller features from the movie.
+ + + +
ImageJ test function¶
To test the ImageJ FFT and determine the best parameters for a given set of movies, run the following function on a test movie matrix:
diff --git a/sitemap.xml b/sitemap.xml index 97481f4..f1a47c9 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1,199 +1,199 @@inputMovieTest = normalizeMovie(inputMovie,'normalizationType','imagejFFT_test'); @@ -2849,10 +2855,11 @@Dark halos around cells¶
If the spatial filter is not properly configured then dark halos will appear around high SNR cells, potentially obscuring nearby, low SNR cells.
-
https://github.com/schnitzer-lab/miniscope_analysis/pull/30
+- FYI, for 4x downsampled movies,
highFreqparameter of 4 (which corresponds to afspecialgaussian with std of 4) produces the closest results to ImageJProcess->FFT->Bandpass Filter...with inputs offilter_large=10000 filter_small=80 suppress=None tolerance=5(the current default innormalizeMovie).
Comparison of MATLAB and ImageJ FFT-based spatial filtering¶
- Example frame from ImageJ and Matlab FFTs.
- This matches the filter that ImageJ says it uses, which is fairly close to the Matlab filter.
-
Example video: 2015_11_25_p384_m610_openfield01
+Example video: Basolateral amygdala miniature microscope imaging in open field
+- Below is an example comparison using the following Matlab commands to produce the filtered inputs:
- Spatial filtering
-
-
- Filtering movies with normalizeMovie -
- Why conduct spatial filtering? +
- Filtering movies with normalizeMovie +
- Images from unit test
- Main filtering functions. @@ -167,6 +167,8 @@
- Common Issues @@ -235,6 +237,14 @@
- I've also added the ability to test the parameter space of the Matlab fft, use the below command.
+
I've also added the ability to test the parameter space of the Matlab fft, use the below command.
testMovieFFT = normalizeMovie(testMovie,'normalizationType','matlabFFT_test','secondaryNormalizationType','lowpassFFTDivisive','bandpassMask','gaussian','bandpassType','lowpass'); -
- - Should get a movie output similar to the below, where there is the original movie, the FFT movie, the original/FFT movie, and the dfof of original/FFT movie.
-
-
- FYI, for 4x downsampled movies,
highFreqparameter of 4 (which corresponds to afspecialgaussian with std of 4) produces the closest results to ImageJProcess->FFT->Bandpass Filter...with inputs offilter_large=10000 filter_small=80 suppress=None tolerance=5(the current default innormalizeMovie).
+ - Example frame from ImageJ and Matlab FFTs.
@@ -317,12 +333,16 @@ - Below is an example comparison using the following Matlab commands to produce the filtered inputs: +
- With some tweaking of the
freqHighand other parameters, should hopefully be able to get closer to macheps and say that the two are identical for our purposes.
Movie Filtering¶
This page documents different features and functions in the CIAtah repository variable for filtering (spatial high/low/bandpass) movies to remove neuropil, cells, or other features.
+Why conduct spatial filtering?¶
+Spatial filtering can have a large impact on the resulting cell activity traces extracted from the movies and can lead to erroneous conclusions if not properly applied during pre-processing.
+For example, below are the correlations between all cell-extraction outputs from PCA-ICA, ROI back-application of ICA filters, and CNMF-e on a miniature microscope one-photon movie. As can be seen, especially in the case of ROI analysis, the correlation between the activity traces is rendered artificially high due to the correlated background noise. This is greatly reduced in many instances after proper spatial filtering.
+
+ + + +Filtering movies with
normalizeMovie¶Users can quickly filter movies using the
normalizeMoviefunction. See below for usage.% Load the movie (be in TIF, HDF5, AVI, etc.). Change HDF5 input dataset name as needed. @@ -262,34 +272,36 @@Filtering movies with
normalIf users set
options.showImages = 0;, thennormalizeMoviewill update a figure containing both real and frequency space before and after the filter has been applied along with an example of the filter in frequency space. This allows users to get a sense of what their filter is doing.
-Why conduct spatial filtering?¶
-Spatial filtering can have a large impact on the resulting cell activity traces extracted from the movies.
-For example, below are the correlations between all cell-extraction outputs from PCA-ICA, ROI back-application of ICA filters, and CNMF-e on a miniature microscope one-photon movie. As can be seen, especially in the case of ROI analysis, the correlation between the activity traces is rendered artificially high due to the correlated background noise. This is greatly reduced in many instances after proper spatial filtering.
-
- - - -Images from unit test¶
Main filtering functions.¶
-Below is a screen grab from a random frame using all the filtering functions. A nice way to quickly see the many differences between each functions filtering. -
+Below is a screen grab from a random frame using all the filtering functions. A nice way to quickly see the many differences between each functions filtering.
+ + +Test function filtering¶
-This function will take a movie and This is currently only for the Matlab fft, but I'll see about expanding to others.
-+unitNormalizeMovie; +This function will take a movie and conduct multiple spatial filtering operations on it then display for the user.
+ + +-ciapkg.unit.unitNormalizeMovie;
+After running that function, below is an example movie from a prefrontal cortex animal (miniature microscope, GCaMP) showing the difference in results with different spatial filtering.
+Matlab test function¶
--
-
Should get a movie output similar to the below, where there is the original movie, the FFT movie, the original/FFT movie, and the dfof of original/FFT movie.
+
+This can also be expanded to look at the effects of different spatial frequency filters on the resulting output, as indicated below.
+Matlab test function movie output¶
Similar to above, showing results when using
-lowpassFFTDivisivenormalization (using thematlab divide by lowpass before registeringoption inmodelPreprocessMovieandviewMovieRegistrationTestfunctions) withfreqLow = 0andfreqHighset to1,4, and20. This corresponds to removing increasingly smaller features from the movie.
+ + + + + +ImageJ test function¶
To test the ImageJ FFT and determine the best parameters for a given set of movies, run the following function on a test movie matrix:
inputMovieTest = normalizeMovie(inputMovie,'normalizationType','imagejFFT_test'); @@ -303,8 +315,12 @@Common IssuesDark halos around cells¶
If the spatial filter is not properly configured then dark halos will appear around high SNR cells, potentially obscuring nearby, low SNR cells.
-https://github.com/schnitzer-lab/miniscope_analysis/pull/30 -* FYI, for 4x downsampled movies,
+ + +highFreqparameter of 4 (which corresponds to afspecialgaussian with std of 4) produces the closest results to ImageJProcess->FFT->Bandpass Filter...with inputs offilter_large=10000 filter_small=80 suppress=None tolerance=5(the current default innormalizeMovie).-
+
Comparison of MATLAB and ImageJ FFT-based spatial filtering¶
Dark halos around cellsThis matches the filter that ImageJ says it uses, which is fairly close to the Matlab filter.
Example video: 2015_11_25_p384_m610_openfield01 -* Below is an example comparison using the following Matlab commands to produce the filtered inputs: +
Example video: Basolateral amygdala miniature microscope imaging in open field
+ + +-
+
+testMovieFFT = normalizeMovie(testMovie,'normalizationType','lowpassFFTDivisive','freqHigh',7); testMovieFFTImageJ = normalizeMovie(testMovie,'normalizationType','imagejFFT'); diffMovie = testMovieFFT-testMovieFFTImageJ ; -Repository statsValidating ce
After users have run cell extraction, they should check that cells are not being missed during the process. Running the method
viewCellExtractionOnMoviewill create a movie with outlines of cell extraction outputs overlaid on the movie.Below is an example, with black outlines indicating location of cell extraction outputs. If users see active cells (red flashes) that are not outlined, that indicates either exclusion or other parameters should be altered in the previous
modelExtractSignalsFromMoviecell extraction step.
+
title: Sorting cell extraction outputs.
diff --git a/pipeline_detailed_signal_extraction_validation/index.html b/pipeline_detailed_signal_extraction_validation/index.html index 0f85b5b..f624042 100644 --- a/pipeline_detailed_signal_extraction_validation/index.html +++ b/pipeline_detailed_signal_extraction_validation/index.html @@ -212,6 +212,7 @@Validating ce
After users have run cell extraction, they should check that cells are not being missed during the process. Running the method
viewCellExtractionOnMoviewill create a movie with outlines of cell extraction outputs overlaid on the movie.Below is an example, with black outlines indicating location of cell extraction outputs. If users see active cells (red flashes) that are not outlined, that indicates either exclusion or other parameters should be altered in the previous
modelExtractSignalsFromMoviecell extraction step.
+
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