I have a task of identifying the number of bacterial colonies on a relatively diverse set of top-down photos of a Petri dish located on a table. The basic process is the following:
- detect the Petri dish on the image, crop everything outside of it;
- apply binary thresholding which should result in a black background and white colonies or clusters thereof;
- use simple blob detector or watershed to identify the colonies, highlight them on the source image and output their count.
Input example 1 Input example 2 Input example 3: an edge case
Problem 1
The table around the Petri dish isn't smooth and contains spots so I usually use Hough transform to detect the dish and remove everything outside of it. The problem is that there are light reflections near the edge of the Petri dish represented as rings with their radius on par with that of the dish edge, as well as other reflections that obscure the view of the colonies and affect the thresholding applied. So I need reliable code for detecting the innermost circle that has roughly the same centre as the outer border of the Petri dish and doesn't contain any further reflections, i.e. cropping at the outer border is sub-optimal.
Cropping attempt
fig. 1. Grab first detected circle with a radius within the range of [int(image.shape[1]/4),int(image.shape[1]/2)] from circle Hough Transform, use a mask and crop to [x+r:x-r,y+r:y-r]
Problem 2.1
The colonies have a colour usually close to the colour of the background (the agar) and in different areas these can overlap (e.g. colony colour in a section A has the same colour as the background in a section B). This renders the method of general thresholding useless. Different photos having different brightness is an issue as well in the context of the binary method and its rigid parameters - for some images a param of (184,255) is useful while on others only a setting as low as (120,255) results in something half-usable.
Gaussian blur, pyrmeanshift, binary threshold
fig 2.1. Gaussian blur (3,3) + pyrmeanshift (6,27) + binary threshold (205,255)
Problem 2.2
The bacterial colonies have round shapes which sometimes form clusters of overlapping circles so the simple blob detector tends to ignore those. The algorithm is supposed to detect the cluster and identify how many colonies (circles) are in it. To tackle this, I've tried Euclidean distance transform coupled with watershed as an alternative to simple blob detector but this needs to be fed a clean image not containing anything other than the colonies themselves, so a robust threshold algorithm is required for removing all the light reflections and eliminating the background's (agar's) gradient. There are also many spots on the Petri dish usually smaller in size than the colonies and not really round - these should be ignored by the detector algorithm. I've heard of adaptive thresholding used for overcoming the problem of a varied background but this tends to convert non-colony small spots on the dish into full-fledged circles which isn't very optimal.
Adaptive threshold - Gaussian method
fig 2.2. Adaptive threshold (Gaussian C)
An attempt at detecting colonies on input example 2
fig 3. A failed attempt at using watershed with distance transform, demonstrating that this algorithm requires a well cleaned-up and properly thresholded input
I'm interested to know whether this task is feasible in the context of a varied collection of photos taken in different lighting conditions as well as different colonies having different sizes and colours. If so, what ways are there to approach this?
