Hi Hannah and Eric
I’m finally on to some real data analysis but am running into some trouble.
I’m screening biofilm on an old Opera high-content screening system which handles dark images by increasing the contrast of the image accordingly. This creates some problems for me, when trying to threshold in BiofilmQ, where the automatic thresholding methods have a real hard time thresholding the dark images (i.e. they include all of the noise).
I’ve tried to adjust both the convolution and the top hat filtering, both the problem challenge persists, also with any auto thresholding method.
These are MaxInt Z-projections with auto B/C (ImageJ - similar to BiofilmQ). From left to right: Blank well (no contents), planktonic cells, biofilm.
Below are the ortho views of a segmentation of these images with Otsu sens = 0.2, conv. kernel = 5, 3, median filtering, and top hat size = 25 vox.
As you can see, the low-contrast images (dark images) are thresholded to include a lot more information than the images with biofilm (which kind of screws up my downstream analysis )
Do you have any suggestions for how I can improve BiofilmQs thresholding of images like these?
I have found thresholding methods in ImageJ that are able to threshold the dark images correctly (MaxEntropy and RenyiEntropy) - can I somehow supply binary images to BiofilmQ and just do the segmentation and parameter calculations?
Or do I simply need to use a different imaging system to acquire better images?
Original tifs for BiofilmQ: