I am working on analyzing a data set and I was wondering what would be the most statistically valid method of demonstrating that there is a strong spatial correlation between images.

I have a data set with about 50 pairs of images of cancerous tissue samples. The first image in each pair shows the locations of gold nanoparticles, and the second image shows the locations of the blood vessels in the same tissue sample. By looking at the images it is easy to see that the locations of the nanoparticles match up with the blood vessels, but I would like to prove this statistically in the paper. This is an important point because it demonstrates that the nanoparticles bind specifically to the cancerous areas instead of the normal tissue.

I have been looking at different statistics such as a simple linear correlation or something more sophisticated like the Moran's I statistic. However, I haven't found anything that would work well for correlation between images.


1 Answer 1


You may interpret both images as individual clustering problems: You can then calculate an a posteriori probability of beeing marked/ blood vessels.

In case both images are properly aligned, you may now calculate a distance measure between the two matrices of a posteriori probabilities. A common distance measure is the Kullback-Leibler divergence.


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