Update colocalization-by-cross-correlation.md

Author: andmccall

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

@@ -32,7 +32,7 @@ Available on the list of [ImageJ updates sites](/update-sites/following). Requir
 To get the best possible results, you will want to create and save a [segmented mask](/imaging/segmentation) for one of your images. This should be a mask of where within the image your want to search for spatial correlations. Generally, the mask should contain all possible localizations for your stains or dyes, or it should be a mask of localization for your null hypothesis (_i.e_ if you hypothesize a protein is localized to the mitochondria, you would want your mask to encompass the entire cell). As an example, say you are studying correlation between two nuclear proteins, then you would want your mask to cover the nucles, which could be created easily using a DAPI or Hoechst stain (the mask itself does not need to be generated from either image your are trying to correlate). If you were studying cytoplasmic proteins, you would want your mask to cover the entire cytoplasm. The mask is very important and not using it could easily lead to undesired correlations. This is because without a mask this plugin will find correlations at any distance, and, if say you are studying nuclear proteins, can easily correlate one nuclei to the nuclei of a neighboring cell (cells are often highly repetitive and spaced relatively evenly). This broad correlation is the low spatial frequency component, and if not corrected for your results will be affected by it. However, when an appropriate mask is used, these cell to cell correlations will be subtracted out during the analysis.
 
 ### Prepare your images
-[Deconvolving] (/imaging/deconvolution) your input images can drastically improve the results of CCC and is highly recommended.
+[Deconvolving](/imaging/deconvolution) your input images can drastically improve the results of CCC and is highly recommended.
 
 It's also best to use an appropriate background subtraction method on the two data images in order to lower the background pixel values. While pixels outside of the masked region do not contribute to the cross-correlation result, having a high signal to background ratio within the masked region will help improve the confidence signifiantly. **Images should be converted to 32-bit depth prior to background subtraction.** This should be done to allow negative values in the image, which will improve the results of the statistical measure. After converting to 32-bit, the mean background value, measured from a region devoid of signal, should be subtracted from the image. For 3D images where the majority of voxels are background, you can use the search bar to run the 'stats.median' ops function, which will give you an appropriate background value for the entire image.