Optimal composite morphological supervised filter for image denoising using genetic programming: Application to magnetic resonance images

Composite filters based on mathematical morphological operators (MMO) are getting considerable attraction in image denoising. Most of such approaches depend on pre-fixed combination of MMO. In this paper, we proposed a genetic programming (GP) based approach for denoising magnetic resonance images (MRI) that evolves an optimal composite morphological supervised filter (FOCMSF) by combining the gray-scale MMO. The proposed method is divided into three modules: preprocessing module, GP module, and evaluation module. In preprocessing module, the required components for the development of the proposed GP based filter are prepared. In GP module, FOCMSF is evolved through evaluating the fitness of several individuals over certain number of generations. Finally, the evaluation module provides the mechanism for testing and evaluating the performance of the evolved filter. The proposed method does not need any prior information about the noise variance. The improved performance of the developed filter is investigated using the standard MRI datasets and its performance is compared with previously proposed methods. Comparative analysis demonstrates the superiority of the proposed GP based scheme over the existing approaches.