diff --git a/all_docs/index.html b/all_docs/index.html index bc01216..a0744ae 100644 --- a/all_docs/index.html +++ b/all_docs/index.html @@ -354,6 +354,12 @@
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)
normalizeMovie
¶Users can quickly filter movies using the normalizeMovie
function. See below for usage.
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/help_spatial_filtering/index.html b/help_spatial_filtering/index.html index a765cba..c56f6c9 100644 --- a/help_spatial_filtering/index.html +++ b/help_spatial_filtering/index.html @@ -149,6 +149,12 @@(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 @@