Figure1. Image capture and processing for the samples under
complicated backgrounds.
Cells in suspensions were deposited into a hemocytometer, and images of
complex background was taken and cropped to 1x1 mm. Representative
images of one area with 16 smallest counting chambers are shown. Panel A
shows the original RGB image captured by microscope. Panel B, the image
was converted to 8-bit and resized. After compression, machine
learning-based ilastik was used to distinguish the background from yeast
cells. Panel C shows the process that a user-defined class label was
attached to the images with complex background. Whereafter, ImageJ macro
was used to optimize the batch of images. Black-and-white images were
presented first. Panel D shows the operation to fill the gap with the
function of ImageJ, which are marked by the red circles. Panel E shows
merging cells split by a single pixel line via the “Watershed”
function, which are marked by red clipper. Area can be used to assess
the objectives in images with ImageJ tool. The Area command was applied
in panel F via the “Analyse Particles” function. After setting the
threshold in the Analyse Particles command, cells counted automatically
are highlighted and numbered in an overlay on the image as indicated in
panel G.