Image edge detection with discretely spaced FitzHugh-Nagumo type excitable elements

This paper presents a computer algorithm of detecting edges from a grey scale image with FitzHugh-Nagumo type excitable elements discretely spaced at image grid points. A previous edge detection algorithm utilising the elements is not applicable to darker intensity areas surrounded by brighter ones; the algorithm fails in detecting edges in the areas. In order to solve the problem in detecting edges in relatively dark areas, we proposed to utilise an intensity inverted image as well as its original one. The proposed algorithm firstly provides a tentative edge map from the original image, and simultaneously provides an additional tentative edge map from the inverted image. Then, the algorithm provides a final edge map by merging the two edge maps. We quantitatively confirm performance of the proposed algorithm, in comparison with that of the previous one and that of the Canny algorithm for an artificial grey scale image not having noise. We furthermore confirm robustness and convergence of the proposed algorithm for a noisy image and real ones. These results shows that the performance of the proposed algorithm is much higher than the previous one and is comparable with the Canny algorithm for a noise-less image, and the proposed algorithm converges for all of the images. However, the proposed algorithm is vulnerable for additive noise, in comparison with the Canny algorithm and the anisotropic diffusion algorithm.