I am new to this forum and hope that I am doing this correctly.
In about a month I will be starting my masters thesis where I’ll be working in a biotechnology lab.
One task I was given is to classify images from cells taken from a microscope: do these cells contain so-called “inclusion bodies” (cells containing white circular shapes which should look somewhat like in the picture, see link down below ) or do they not. If a picture contains inclusion bodies, the image should be classified as “positive” and vice versa. For a start, it doesn’t have to quantify the amount of positive cells yet.
Inclusion bodies are basically aggregated (not fully folded/unfolded) proteins that can form under bacterial overexpression of genes/stress conditions. Theoretically they can be stained with reporter gene proteins so that under the fluorescence microscope they appear colored (e.g. green). However, we will first try to classify without staining, so that the image will appear more or less colorless.
According to my supervisor, choosing Python as a programming language would be a good start (main reason is that my colleagues will all be Python-users). Besides of that I got familiar with Python recently and would really enjoy using it. However, I have never done such a thing and would have to start from zero.
Getting back to my question(s): what would be the best approach to develop such a classifier? Is Python even a good approach? Do Python modules such as OpenCV do the job or would they only complicate the process? Are machine learning methods suitable for such a task (unfortunately I don’t know the exact amount of data available)? Is there open-source programs that easily accomplish such tasks or is the programming-approach a better option? The classification should be reproducible, since cells do not always look the same, let alone when they are of different genus/type. However, industry standards do not apply. We just need a high-throughput method to automate classification.
I would appreciate some suggestions, since you people are experienced with image analysis. If you aren’t fully sure it would also be helpful if you could throw out some ideas (e.g. approaches such as “Mexican Hat Algorithm”).
Thank you and kind regards,