Scalar Parameters Optimization in PDE Based Medical Image Denoising by using Cellular Wave Computing

Abstract

In order to help with biomedical images, a set of complex and effective mathematical models are available, based on the PDE (PDE - partial differential equation). On one hand, effective implementation of these methods is difficult, due to the difficulty of determining the scalar parameter values, on which the image processing efficiency depends, while on the other hand, due to the considerable computing power needed in order to perform in real time. Currently there are no analytical and / or experimental methods in the literature for the exact values determination of the scaled parameters to provide the best results for a specific image processing. This paper proposes a method for optimizing the values of a scaling parameter set, which ensure effective noise reduction of medical images by using cellular wave computing. To assess the overall performance of noise extraction, the error function (quantitative component) and direct visualization (qualitative component) are used at the same time. Moreover, by using this analysis, the degree to which the CNN templates are robust against the range of values of the scalar parameter, is obtainable.

Keywords

• PDE based image denoising
• cellular wave computing