Background

One of the most common tasks that takes place in microbiology labs is the counting of bacterial colonies that grow on agar plates. The most basic way to accomplish this task is by manually counting each colony that is present on the plate with a clicker, shown below in Figure 1. However, this is extremely time consuming, requires little thought from the highly trained microbiologists, and there is significant room for human error.

Figure 1: A clicker and a marker are used to manually count bacterial colonies on an agar plate [1].

A slightly more advanced manual counting technology utilizes a hatched grid and a light source (overhead or below) that a transparent agar plate is placed on top of. Moving square by square, the microbiologist will tap each individual colony with a stylus. Each tap signals a noise or vibration from the apparatus that verifies the counting of each tapped colony. The device stores the total number of taps as the number of colonies present on the plate. This process is still quite time consuming, and may only slightly eliminate any human error. Additionally, it still takes microbiologists away from more skilled tasks. An example manual colony counter is pictured below in Figure 2.

Figure 2: A more advanced manual colony counter with an attached overhead light. The circular region contains a hatched grid with which a stylus is tapped on each visible colony and the machine registers each tap and stores a count that will ultimately be the number of bacterial colonies present [2].

The most advanced labs may acquire automated colony counters, but these tend to be extremely expensive, error-prone, and not user-friendly [3]. These automated colony counters operate by  capturing an image of the plate, and then using different image filtering techniques, such as altering contrast values and edge detection, to process the image and count the number of colonies present. An example automated colony counter is shown below in Figure 3.

Figure 3: Automated colony counter, with output interface shown behind. Captures image of plate, analyzes and filters image, and returns the number of colonies as well as an image of what the device determined to be colonies [4].

Testimonials from current microbiologists show that these automated versions are not ideal, yet clearly neither is manual counting. There is a significant need for a bacterial colony counting method that is affordable, accurate, and efficient, and this project hopes to work towards development of such a method [5].