Autofluorescence is a long-standing problem that has hindered fluorescence microscopy image analysis. To address this, we have developed a method that identifies and can exclude autofluorescent signals from multi-channel images post acquisition.
library(devtools)
devtools::install_github("ellispatrick/AFid")library(AFid)
#> Loading required package: moments#> Loading required package: EBImage
set.seed(51773)
## Read in images.imageFile1= system.file("extdata","ImageB.CD3.tif", package="AFid")
imageFile2= system.file("extdata","ImageB.CD11c.tif", package="AFid")
im1<-EBImage::readImage(imageFile1)
im2<-EBImage::readImage(imageFile2)
## Rescale the images.im1=im1/max(im1)
im2=im2/max(im2)
combined<-EBImage::rgbImage(green=sqrt(im1), red=sqrt(im2))
EBImage::display(combined, all=TRUE, method='raster')
## Create masks using EBImage.# Find tissue areatissue1=im1>2*min(im1)
tissue2=im2>2*min(im2)
# Calculate thresholdsimThreshold1<- mean(im1[tissue1]) +2*sd(im1[tissue1])
imThreshold2<- mean(im2[tissue2]) +2*sd(im2[tissue2])
# Calculate masks.mask1<-EBImage::bwlabel(im1>imThreshold1)
mask2<-EBImage::bwlabel(im2>imThreshold2)
## Calculate intersection maskmask<- intMask(mask1,mask2)
## Calculate textural features.df<- afMeasure(im1, im2, mask)
#> Warning in cor(im1Pixels, im2Pixels): the standard deviation is zero#> Warning in cor(im1Pixels, im2Pixels): the standard deviation is zero#> Warning in cor(im1Pixels, im2Pixels): the standard deviation is zero#> Warning in cor(im1Pixels, im2Pixels): the standard deviation is zero#> Warning in cor(im1Pixels, im2Pixels): the standard deviation is zero## Alternatively## Correlation only# afMask <- afIdentify(mask, df, minSize = 100, maxSize = Inf, corr = 0.6)## Clustering with given k# afMask <- afIdentify(mask, df, minSize = 100, maxSize = Inf, k = 6)## Clustering with estimated k.afMask<- afIdentify(mask, df, minSize=100, maxSize=Inf, k=20, kAuto=TRUE)
## Exclude autofluorescence from images (Use ImageJ for halo removal)im1AFExcluded<-im1im2AFExcluded<-im2im1AFExcluded[afMask!=0] <-0im2AFExcluded[afMask!=0] <-0combinedExcluded<-EBImage::rgbImage(green= sqrt(im1AFExcluded), red= sqrt(im2AFExcluded))
img_comb=EBImage::combine(combined, combinedExcluded)
EBImage::display(img_comb, all=TRUE, method='raster',nx=2)
## Or## Exclude AF ROIsexclude1= unique(mask1[afMask>0])
mask1Excluded=mask1mask1Excluded[mask1Excluded%in%exclude1] =0exclude2= unique(mask2[afMask>0])
mask2Excluded=mask2mask2Excluded[mask2Excluded%in%exclude2] =0## OR## A more agressive strategy to exclude autofluorescence from images as an alternative to the halo removal implemented in ImageJim1AFExcludedv2<-im1im2AFExcludedv2<-im2im1AFExcludedv2[!mask1%in%mask1Excluded|!mask2%in%mask2Excluded] <-0im2AFExcludedv2[!mask1%in%mask1Excluded|!mask2%in%mask2Excluded] <-0combinedExcludedv2<-EBImage::rgbImage(green= sqrt(im1AFExcludedv2), red= sqrt(im2AFExcludedv2))
img_combv2=EBImage::combine(combined, combinedExcludedv2)
EBImage::display(img_combv2, all=TRUE, method='raster',nx=2)