Semantic Segmentation: How to evaluate the noise influence of the effectivity and robustness of the medical image segmentation?

Question

I have done some pre-processing including N4 Bias correction, noise removal and scaling on medical 3D MRIs, and I was asked one question:

How to evaluate the noise influence of the effectivity and robustness of the medical image segmentation? When affecting the image structure with various noise, the extracted features will be deteriorated. Such effect should be taken advantage in the context of the method effectivity for different noise intensity.

How to evaluate the noise affect and how to justify the noise removal method used in the scientific manuscript?

Answer 1

I don't know if this can be helpful but I did once in classrom with nuclear magnetic resonance. In that case we use the Shepp Logan Phantom with FFT. then we add noise to the picture (by adding random numbers with gaussian distribution). When you transform the image back to the phantom you can see the effects of noise and sometimes artifacts (mostly due to the FFT algorithm and the window function choosed).

What I did was check the mean value of color in the image before and after, then on edges of the pahntom (skull) you can see how much is clear the passage from white to black and vice versa.

This can be easily tested with MATLAB code and the phantom. When you have the accuracy you need you can then apply the algorithm you choose on real images.