An optimization for adaptive multi-filter estimation in medical images and EEG based signal denoising. (April 2023)

• Record Type: Journal Article
• Authors: Srivastava, Vishal
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: Meta-heuristics -- FPA optimization -- Denoising -- Imaging and EEG data -- Noise removal; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104513
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Classical denoising techniques are efficient to extinguish the Gaussian noise but are unable to handle the impulse and additive noise. The blurring of edges in denoised data is critical in both images and signals. It has been found that edge blurring occurs due to limited spatial information. Adaptive multi-filtering methods can accommodate large spatial information. But the estimation of filter window sizes and their subsequent information fusion is a challenging task. To overcome this issue, an adaptive multi-filter estimation technique that fuses large information has been explored. The fusion function is formulated for multiple sizes of the filter as an objective function. The objective function takes the spatial information from the neighborhood of noisy data using multiple filters and fuses them while minimizing the objective function. The objective function is optimized for various sizes of filter windows with a stochastic flower pollination Algorithm (FPA). Impulse noise is efficiently removed using optimized filters. An empirical study is conducted with recent state-of-the-art using evaluation indices such as MSE, PSNR, MAXERR, L2RAT, SSIM, computational time, and complexity. For the proposed setup, the MSE, PSNR, MAXERR, L2RAT, and SSIM are evaluated as 1.33, 26.90, 201, 0.9964, 0.8922, and 0.9312 respectively that are outperforming the results obtained in the previous state-of-the-art denoising algorithms such as wavelet, compression, box averaging, tabu search and simulated annealing. Further, it is interesting to observe the improvement in complexity. The experimental study demonstrated the performance of the framework with previous qualitative indices in both Alzheimer's images and EEG-based neural signal data. Highlights: FPA based evolutionary algorithm for denoising. The objective hybridization is performed for global optimization. Experimental analysis for MRI and EEG based neural signals. Comparative analysis with recent state-of-arts techniques.