Multi-Domain Medical Image Enhancement Through Fuzzy and Regression Neural Network Approach
DOI:
https://doi.org/10.30865/klik.v4i6.1875Keywords:
Medical Images; Laplacian Filter; PSNR; Deep Learning; Neuro-fuzzyAbstract
Medical image processing has heralded a significant transformation in contemporary medical science, offering the promise of diagnosing, treating, and curing patients while minimizing adverse effects. By leveraging medical imaging, physicians gain the ability to visualize internal structures without invasive procedures. Moreover, this technology contributes to our understanding of neurobiology and human behavior, with brain imaging aiding investigations into addiction mechanisms. Interdisciplinary collaboration among biologists, chemists, and physicists is facilitated by medical imaging, with resultant technologies finding applications across various fields. This study focuses on enhancing medical images in both frequency and time domains. Contrast enhancement is achieved through local transformation histogram techniques, followed by overall enhancement using a Fuzzy-Neural approach. The proposed methodology is implemented using MATLAB 2018b. The findings emphasize the efficacy of the proposed technique in improving image quality for both MR and Selenography images. Its outstanding performance, marked by a higher PSNR (32.96) and a lower MSE (20.04), indicates its potential for more precise and dependable image enhancement compared to current methods.
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