Update colocalization-by-cross-correlation.md

Author: andmccall

_pages/plugins/colocalization-by-cross-correlation.md

@@ -134,7 +134,7 @@ First, this plugin applies the provided mask to both images, setting any pixels
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 To remove the contribution from low spatial frequency structures/data (such as cells and nuclei), a second cross-correlation image generated from a low spatial frequency image is then subtracted from the original correlation 
-image. For this process, the mean intesity of all the pixels of the first input image within the mask is calculated, then a new image is created where all the pixels within the mask are set to this mean value. This image is an averaged low spatial frequency image of your first input image. This low-frequency image is then cross-correlated with the second input image, and the resulting cross-correlation image is the low-frequency component, which is  subtracted from the original cross-correlation image. Then, we generate a radial profile of the subtracted data and fit a Gaussian curve to it. We also generate a radial profile for the original correlation data before subtraction, as this is needed to establish a measure of confidence. The confidence is calculated as the area under the curve (AUC) of the subtracted correlation radial profile (in the range of mean ± 3×sigma) divided by the AUC of the original correlation radial profile (in same range). The confidence value, along with the mean and sigma of the Gaussian fit are displayed in a log window. Higher values of confidence, closer to 1, indicate that two images likely have a true spatial correlation at the indicated distance.
+image. For this process, the mean intesity of all the pixels of the first input image within the mask is calculated, then a new image is created where all the pixels within the mask are set to this mean value. This image is an averaged low spatial frequency image of your first input image. This low-frequency image is then cross-correlated with the second input image, and the resulting cross-correlation image is the low-frequency component, which is  subtracted from the original cross-correlation image. The mean under the mask process is only applied to one image as applying it to both did not result in a significant difference in results and uses more time and memory. Then, we generate a radial profile of the subtracted data and fit a Gaussian curve to it. We also generate a radial profile for the original correlation data before subtraction, as this is needed to establish a measure of confidence. The confidence is calculated as the area under the curve (AUC) of the subtracted correlation radial profile (in the range of mean ± 3×sigma) divided by the AUC of the original correlation radial profile (in same range). The confidence value, along with the mean and sigma of the Gaussian fit are displayed in a log window. Higher values of confidence, closer to 1, indicate that two images likely have a true spatial correlation at the indicated distance.
 
 To generate the contribution images, we further modify the subtracted cross-correlation image, by effectively multiplying it with the Gaussian fit in order to create a cross-correlation image that only retains the data within our Gaussian curve range. This Gaussian-modified cross-correlation image is then used to back-calculate the contribution images. Image1Contribution = (image2 ∗ gaussModifiedCorr) × image1. Image2Contribution = (image1 ★ gaussModifiedCorr) × image2. Key: ∗ -> convolve, ★ -> correlate.