Detect edges of medical images using entropic filter and fractal dimension

Abstract

This research aims to use a number of techniques or filters to obtain an image of more quality than the original images. Edges add specific elements to the image, which increases the idea of the independence of the fractal dimension as a measure of self-similarity in partial structures. Therefore, the local degree of fractal dimension is used to differentiate the edges from the inner piece and from noise.
The method was tested by comparing the fractal edge detector with conventional filters such as an entropic filter
Entropy filter. The results showed by calculating the signal-to-noise ratio (S N R) the advantage of using the fractal dimension due to its work on obtaining clear and accurate edges and an image with improved features and high quality of a blurred image compared to the entropic filter technique.