From ee9f45d4c907d69e7f4b1699e26adcdd020d3b6d Mon Sep 17 00:00:00 2001
From: Biafra Ahanonu 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. (Chebychev clustering, n = 5 clusters) Users can quickly filter movies using the If users set If users set Bandpass filtering where only Another method is 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.
@@ -2767,7 +2773,7 @@
Filtering movies with
normalizeMovie
¶normalizeMovie
function. See below for usage.options.showImages = 0;
, then normalizeMovie
will 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.options.showImages = 0;
, then normalizeMovie
will 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. See below for examples.FFT bandpass filtering¶
+red
frequencies in filter
image (FFT of bottom left input image) are kept producing an image as in fft image
.Divide by lowpass filtering¶
+lowpassFFTDivisive
, which involves dividing the image by a lowpass version of itself. In the below example, the filter
image shows that only low frequencies will be kept. This will produce an image as in fft image
that when divided or subtracted from the input image
will produce difference
image.Images from unit test¶
Main filtering functions.¶
(Chebychev clustering, n = 5 clusters)
normalizeMovie
¶normal
% Run analysis
inputMovie = normalizeMovie(single(inputMovie),'options',options);
-If users set options.showImages = 0;
, then normalizeMovie
will 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.
If users set options.showImages = 0;
, then normalizeMovie
will 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. See below for examples.
Bandpass filtering where only red
frequencies in filter
image (FFT of bottom left input image) are kept producing an image as in fft image
.
+
Another method is lowpassFFTDivisive
, which involves dividing the image by a lowpass version of itself. In the below example, the filter
image shows that only low frequencies will be kept. This will produce an image as in fft image
that when divided or subtracted from the input image
will produce difference
image.
+
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.
diff --git a/index.html b/index.html index 9850cbe..63362f6 100644 --- a/index.html +++ b/index.html @@ -404,5 +404,5 @@