These are fixed red blood cells. The task is to count them with Pixcavator. They average 7.7 microns in size and were photographed unstained with differential interference contrast lighting. I had to crop the image to ensure reasonable processing times.

The quality of the image is good, but there is still a problem with the image. Each cell is captured by two light semicircles. These two semicircles aren’t connected to each other however (because the light comes from one direction?), so there are no full circles. The result is that cells can’t be treated as objects and they aren’t captured by the software. In the left image, there should be red contours for each of the cells like in the image on the right.

One way to get around this is to count the semicircles themselves (2 per cell). I ran Pixcavator with the following settings: 1000 for area and 100 for contrast.

The problem with counting semicircles is that many of some of them touch each other so that they form clusters. These clusters are what’s captured by Pixcavator. To deal with this problem I needed some extra computation that followed the analysis. In the last column of the saved spreadsheet (table below) I divided the areas of the clusters of semicircles by the area of one semicircle (“1030”). The total number of semicircles found this way was 35. Then the estimate was 17.5 versus 17 of manual count.

Another way to handle the problem is to start with some preprocessing. Erosion makes the light semicircles grow, they merge and form circular regions. Inside of those lie dark objects captured by Pixcavator. They correspond to cells.

I did 15 rounds of erosion (I had to use Pixcavator’s feature because ImageJ does erosions for binary images only). 15 is a lot as you can see.

Then I analyzed the image with the following settings: contrast 27, saliency 6768. The erosions, however, created several artifacts that had to be unmarked.

This method is more straightforward. With it, however, it is harder to get good results without manual intervention.

Live cells in the next post.

For other examples, see our wiki.