Continuing the discussion from Image J binarization, skeletonizing and fractalizing:
I will try to make myself clearer and try to explain better.
We have a 2 dimensional picture with 255 grey values:
They derive from the same place within the center of the retina in different layers (because of course the retina is a 3 dimensional tissue) - so they are overlays of the same vascular network the so called superficial capillary plexus (SCP) and the deep capillary plexus (DCP)
They are 6x6 mm and have a transversaal resolution betweem 9-18 microm/pixel depending on the scan quality.
The disease we investigate is unilateral, develops in the DCP extending to the SCP. Its major hallmark is a loss of capillaries around the center of the retina - the foveolar avascular zone. So theoretically, there must be a difference between the density of the vascular network around the center of the fovea of the ill and the healthy eye (which was proven by the study you read). We use a newer technology and believe that there is not only a difference between the affected and the contralateral eye but we think (it looks like) there is a difference between the “healthy” eye and a sample of unaffected patients.
In order to express this difference, we try to come up with our own algorithm by a different technology of much higher resolution. Nevertheless, the principle is similar. We want to binarize the original picture with a threshold ideal to catch most of the smallest vessels and their intervascular spaces:
Next step is to skeletonize the whole picture into as many intervascular spaces as detectable based on the binarized vessels - this is where we made the first mistake I believe:
If you take a look at the foveal center and compare it to the binarized picture above, there should not be any small “fractals” at all. Also, we want to get rid of the bigger vessels, and other artefacts, which would manpiulate our data. We couldn’t do that properly:
We wanna measure the number and size of areas split by the vessels (simplified: the more numerous and the smaller, the higher density) and compare the density in areas of interest
The goal would be a relative number of the density within the ring-shaped area as opposed to the density of the whole picture. In theory, this relation should be smaller the monre “normal” the vascular density within the ring is.
We were not able to quantify the number and size of small “fractals” within certain areas as tried below:
Does it become more meaningful to you now?
I’m sorry if I have problems with the explanation, I am a trained ophthalmologist with an interest in imaging but no clue about I.T. If you do have an impression on what I mean, it would make most sense if I wrote to you tomorrow someday whenever suits you best. I will try to get in touch with my brother, who is more of a computer guy. Is that ok with u?
Thanks a lot anyway, it seems like a typical “M.D.” scientist problem, but we tried everything including to contact the other study group without success.