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Wednesday, 16 December 2015

Counting cell nuclei in an image

I have been working with a colleague, Dr Joaquin de Navascues from the European Cancer Stem Cell Research Institute, to develop a workshop entitled an "Introduction to Biological Image Analysis". We plan to discuss the fundamentals of digital images and how to work with them in FIJI (Image J) and R, two open source data analysis tools. We aim to deliver this workshop during March or April next year at Cardiff University.

Joaquin is taking the lead on working with FIJI and I am developing the R material. For biological image analysis there is a useful package called EBImage. This has a nice introduction available and a detailed handbook - (version: 4.13.5).

I have been using this package to count cell nuclei in an image. The image is a microscope picture of a Drosophila gut. Counting nuclei involves mathematical transformations of the digital data. A digital image is a matrix of numbers.

In non-technical language the key steps are:
  • blur the image 
  • apply a threshold to turn nuclei into 'blobs'
  • count the 'blobs'
The output from this script is:

Number of nuclei in this image = 92

The script below downloads an image from Github, opens the image, displays it, transforms it and then counts the nuclei. Because I plan to count nuclei from more than one image, I have made a function and then applied it to the downloaded file. Using user defined functions to automate your workflow is a very good use of R. 


# to install use this:
# source("http://bioconductor.org/biocLite.R")
# biocLite("EBImage")

library(EBImage)  # you might need to install - see above

# the image is on Github 
# it is from a set of cells that are stained to detect the nuclei
# this is the link to the data

link <- "https://raw.githubusercontent.com/brennanpincardiff/RforBiochemists/master/data/seq/seq_z015_c003.tif"

# the download.file() function downloads and saves the file
download.file(url=link, destfile="file.tif", mode="wb")

# EBImage uses the readImage() function to load the file. 
img1 <- readImage("file.tif")

display(img1, method = "raster")  # shows the image within R. 

display(img1*4, method = "raster") # multiply the image to make brighter 

# I have written a function to count nuclei
# includes blurring the image, applying a threshold and counting....
# it displays the image as it is changed. 
# it's not perfect and overestimates the number of nuclei. 
# it's an example that can be done. 
# improving and customizing the various options is very feasible. 

countNuclei <- function(img1){   
  # blur the image
  w = makeBrush(size = 11, shape = 'gaussian', sigma = 5)  # makes the blurring brush
  img_flo = filter2(img1*2, w) # apply the blurring filter
  display(img_flo * 4, method = "raster") # display the blurred image - brighter for display only. 

  # apply a threshold 
  nmaskt = thresh(img_flo *2, w=10, h=10, offset=0.05) 
  display(nmaskt, method = "raster")

  # the bwlabel() function 'counts' the blobs
  nucNo <- max(bwlabel(nmaskt))

  # this outputs the count to us
  cat('Number of nuclei in this image =', max(bwlabel(nmaskt)),'\n')

# this applies the function to the image
nucNo <- countNuclei(img1)


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