Fast Method Based on Fuzzy Logic for Gaussian-Impulsive Noise Reduction in CT Medical Images

Josep Arnal () and Luis Súcar
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Josep Arnal: Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Campus de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Spain
Luis Súcar: Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Campus de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Spain

Mathematics, 2022, vol. 10, issue 19, 1-18

Abstract: To remove Gaussian-impulsive mixed noise in CT medical images, a parallel filter based on fuzzy logic is applied. The used methodology is structured in two steps. A method based on a fuzzy metric is applied to remove the impulsive noise at the first step. To reduce Gaussian noise, at the second step, a fuzzy peer group filter is used on the filtered image obtained at the first step. A comparative analysis with state-of-the-art methods is performed on CT medical images using qualitative and quantitative measures evidencing the effectiveness of the proposed algorithm. The parallel method is parallelized on shared memory multiprocessors. After applying parallel computing strategies, the obtained computing times indicate that the introduced filter enables to reduce Gaussian-impulse mixed noise on CT medical images in real-time.

Keywords: CT images; fuzzy logic; fuzzy metric; medical image enhancement; mixed impulsive and Gaussian noise; noise reduction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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