diffusion model for 2D images

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Nisreen Sulayman
Nisreen Sulayman on 1 Oct 2014
I have a discussion with Image Processing researcher about using diffusion model for 2D images:
He said ... "the anti-concentration diffusion model is wrong for 2D digital subtraction angiography DSA images. Because in 2D, pixel-values (concentration) are summations in depth, and small distances in 2D can be large in 3D."
I said that 2D DSA images suffer from intensity inhomogeneity and we can apply diffusion models to correct intensity inhomogeneity problem.
What do you think?

Answers (1)

Bjorn Gustavsson
Bjorn Gustavsson on 1 Oct 2014
If I get this right...
Linear diffusion you can calculate by straightforward convolution with a Gaussian kernel, and that operation should commute with the integration along the lines-of-sight your imaging system does. If you start doing non-linear diffusion this will no longer be true - you would not get the same result if you first did a non-linear diffusion of the 3-D object and then imaged that, as if you first imaged the 3-D object and then did non-linear diffusion on that. So it depends on what type of diffusion you do. This is resting on the fact that the diffusion is done with a Gaussian kernel and that those are separable. This means that you can separate the diffusion perpendicular to and along the lines-of-sight.
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Bjorn Gustavsson
Bjorn Gustavsson on 2 Oct 2014
Except for the case where DSA-images are of the same kind as X-ray tomography - then its different.

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