Hi Masters of Image Processing!
I’ve recently wanted to use CARE on phase contrast (PH) images, and I am wondering what the best way to normalize these images is.
The built-in quantile-based way works fine but I have found that due to the uneven nature of the background in PH, creating patches for training benefits from including the background.
But when there are background patches with no signal, quantile-based normalization exaggerates the background.
I tried using a fixed-value normalization (Just dividing by an arbitrary value) to ensure the data lies between [0;1] but I think I could do better…
One idea is to use a mode or median-based normalization. That is, considering that in my images there will always be over 50% of background, I can
- Smooth the image slightly
- Compute the mode (or median)
- Divide the image by this value times an arbitrary value
I looked around different papers working on phase contrast images, but most normalize the data after some initial processing of the PH image (after a Laplacian or Hessian or Gradient) and none mention how the PH image could be normalized.
Do you think that the median-based approach makes sense? What other methods have you used/know of?
Thanks for any input!
[EDIT : Post was sent before it was completed]