New anisotropic diffusion operator in images filtering
Miguel Ángel Vera, Elizabeth B. Gonzalez, Yoleidy Huérfano, Elkin Gelvez-Almeida, Oscar Valbuena · Journal of Physics Conference Series · 2020
Abstract The anisotropic diffusion filters have become in the fundamental bases to address the medical images noise problem. The main attributes of these filters are: the noise removal effectiveness and the preservation of the information belonging to the edges that delimit the objects of an image. Due to these excellent attributes, through this article, a comparative study is proposed between a new diffusion operator and the Lorentz operator, proposed by the pioneers of anisotropic diffusion. For this, a strategy consisting of two phases is designed. In the first, called operator construction, the composition of functions is used to generate a new diffusion operator that meets with the conditions reported for this kind of the mathematical object. In the second phase, denominated filtering, a synthetic cardiac images database, based on computed tomography, is filtered using the aforementioned operators. According with the value obtained for the peak of the signal-to-noise ratio, the new operator shows similar performance to the Lorentz operator. The implementation of this new operator contributes to the generation of new knowledge in digital image processing context.