Adapted from: https://www.bioconductor.org/packages/release/bioc/vignettes/EBImage/inst/doc/EBImage-introduction.html

On Ubuntu install requires sudo apt install r-cran-fftwtools from marutter’s ppa

install.packages("BiocManager")
BiocManager::install("EBImage")

library(EBImage)

Reading

• Let’s look at some microscope images of cells

nuclei_file <- system.file("images", "nuclei.tif", package="EBImage")
nuclei_img <- readImage(nuclei_file)
nuclei_img

• Image object includes information on
• Color mode - Gray scale
• Dimensions - 510 x 510 pixels
• Frame is like a layer in spatial data
  • Can be frames in a movie, different images, or different color bands
  • If this was a single color image would have
    • frames.total = 3 for red, green, blue
    • frames.render = 1 for a single combined image
• Images are encoded as numbers for each pixel
• For grayscale: range from 0 (black) to 1 (white)
• Data is stored in a 3D matrix
  • x
  • y
  • frame

Display

• Can show image using plot or display
• plot makes a simple graph of the first frame

plot(nuclei_img)

• display allows interactive viewing

display(nuclei_img)

Cropping

• Because an image is just a matrix of numbers
• Cropping is like subsetting a matrix

nuclei_img_cropped <- nuclei_img[200:300, 350:450, 1]
display(nuclei_img_cropped)

• Just work with one image for simplicity
• Get all rows and columns for a single frame

nuclei_img = nuclei_img[,,1]
nuclei_img

Thresholding

• Distinguish between background and objects
• We can see which areas are nuclei and which aren’t
• But the computer just sees continuous numbers
• Even in areas that appear clearly black the numbers aren’t zero

nuclei_img[220:225, 220:225]

• Thresholding makes pixels binary
  • True or False
  • Part of a nuclei or not
• Can do this manually

nuclei_thresh <- nuclei_img > 0.5
display(nuclei_thresh)

• But determining the right threshold can be tricky
• Our first try left some nuclei out
• Try a lower threshold

nuclei_thresh <- nuclei_img > 0.1
display(nuclei_thresh)

• Leaves too many pixels in

• Automatically select threshold with Otsu’s method

  • Clusters pixels by intensity to get a better result

threshold <- otsu(nuclei_img)
nuclei_thresh <- nuclei_img > threshold
display(nuclei_thresh)

• Adaptive thresholding- vary threshold by location in image

Image segmentation

• Split the image up into unique objects
• One simple way is to group all contiguous threshold pixels into an object

nuclei_segmented <- bwlabel(nuclei_thresh)

• Labels each distinct nuclei with a different number (class)

nuclei_segmented
nuclei_segmented@.Data[1:15, 1:15]

• Can them display them with different colors

display(colorLabels(nuclei_segmented))

Counting objects

• With this segmentation we can then count the number of nuclei
• One unique number for each nuclei so the biggest number is the number of nuclei

max(nuclei_segmented)

• 98 nuclei in this image

Measuring

• Measure the location and features of the objects
• computeFeatures
• Information on shape and size

computeFeatures.shape(nuclei_segmented)

• Information on location

computeFeatures(nuclei_segmented, nuclei_img)

• Measures are in pixels
• Include an object of known size to convert to meaningful units

Conclusions

• Threshold -> Segment
• Lets us count and measure objects in an image