A Parallel Fuzzy Algorithm for Real-Time Medical Image Enhancement

Medical images may be corrupted by noise. This noise affects the image quality and can obscure important information required for accurate diagnosis. Effectively apply filtering techniques can facilitate diagnosis or reduce radiation exposure. In this paper, we introduce a parallel method designed t...

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Bibliographic Details
Published in:International journal of fuzzy systems Vol. 22; no. 8; pp. 2599 - 2612
Main Authors: Arnal, Josep, Chillarón, Mónica, Parcero, Estíbaliz, Súcar, Luis B., Vidal, Vicente
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computational Intelligence
Decomposition
Diagnosis
Digital imaging
Engineering
Fuzzy logic
Fuzzy sets
Graphics processing units
Image enhancement
Image quality
Management Science
Medical imaging
Methods
Noise
Operations Research
Radiation
Radiation effects
Random noise
Real time
Similarity measures
X-rays
Medical image processing
Fuzzy logic
Filter design
Noise reduction
ISSN:1562-2479, 2199-3211
Online Access:Get full text
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Summary:Medical images may be corrupted by noise. This noise affects the image quality and can obscure important information required for accurate diagnosis. Effectively apply filtering techniques can facilitate diagnosis or reduce radiation exposure. In this paper, we introduce a parallel method designed to reduce mixed Gaussian-impulse noise from digital images. The method uses fuzzy logic and the fuzzy peer group concept. Implementations of the method on multi-core interface using the open multi-processing (OpenMP) and on graphics processing units (GPUs) using CUDA are presented. Efficiency is measured in terms of execution time and in terms of MAE, PSNR and SSIM over medical images from the mini-MIAS database and over computed radiography (CR) images generated at different exposure levels. These images have been contaminated with impulsive and/or Gaussian noise. Experiments show that the proposed method obtains good performance in terms of the above mentioned objective quality measures. After applying multi-core and GPUs optimization strategies, the observed time shows that the new filter allows to remove mixed Gaussian-impulse noise in real-time.
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ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-020-00953-3