A Bio-inspired Parallel-Framework Based Multi-gene Genetic Programming Approach to Denoise Biomedical Images

The occurrence of noise is a common problem in biomedical imaging applications. The denoising of corrupted biomedical images is a challenging task. In this paper, we present a biologically inspired parallel-framework based multi-gene genetic programming (MGGP) approach for denoising biomedical images from mixed impulse noise. Our biologically inspired approach has achieved an improved denoising performance by exploiting its parallel framework of multiple genes modeling capability in noise detection and removal stages. In the detection stage, we developed MGGP-based noise detector using rank-ordered and robust statistical features to effectively locate the corrupted pixels and generate noise map. In the noise removal stage, the detected noisy pixels are denoised by developing a bio-inspired MGGP-based estimator using statistical features of only noise-free pixels in their neighborhood. Extensive experimentation is carried out to demonstrate the robust performance of the proposed approach on diverse types of biomedical images corrupted with different noise densities. As a test case, we evaluated the performance of the proposed bio-inspired approach for benchmark biomedical images of Algae, C05c, Celulas, Crm04280, Crm05210, Nemacb1, Nemacl2, MRI, X-ray, Heart and microscopic images of fungal spores causing wheat rust. The proposed parallel-framework based bio-inspired approach has demonstrated an improved performance over other existing conventional and bio-inspired learning approaches.