Introduction:

We propose a novel approach for the assessment of the similarity of retinal vessel segmentation images that is based on linking the standard performance metrics of a segmentation algorithm, with the actual structural properties of the images through the fractal dimension.

Method:

We apply our methodology to compare the vascularity extracted by automatic segmentation against manually segmented images.

Results:

We demonstrate that the strong correlation between the standard metrics and fractal dimension is preserved regardless of the size of the subimages analyzed.

Discussion or Conclusion:

We show that the fractal dimension is correlated to the segmentation algorithm’s performance and therefore it can be used as a comparison metric.