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Article

Parameter Optimization for Local Polynomial Approximation based Intersection Confidence Interval Filter Using Genetic Algorithm: An Application for Brain MRI Image De-Noising

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Department of Information Technology, Techno India College of Technology, Kolkata 700156, India
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Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta 31527, Egypt
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College of CIT, Taif University, Ta'if, Saudi Arabia
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Computer Science Department, College of Computers & Information Technology, Taif University, Ta'if 21974, Saudi Arabia
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Department of Computer Science, Karpagam University, Coimbatore 641021, India
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Department of Social Medicine, University of Crete, Crete 60417, Greece
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Institute of Systems Engineering and Robotics, Bulgarian Academy of Sciences, Sofia 1000, Bulgaria
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto 4200-465, Portugal
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Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 19 August 2015 / Revised: 16 September 2015 / Accepted: 17 September 2015 / Published: 25 September 2015
Magnetic resonance imaging (MRI) is extensively exploited for more accurate pathological changes as well as diagnosis. Conversely, MRI suffers from various shortcomings such as ambient noise from the environment, acquisition noise from the equipment, the presence of background tissue, breathing motion, body fat, etc. Consequently, noise reduction is critical as diverse types of the generated noise limit the efficiency of the medical image diagnosis. Local polynomial approximation based intersection confidence interval (LPA-ICI) filter is one of the effective de-noising filters. This filter requires an adjustment of the ICI parameters for efficient window size selection. From the wide range of ICI parametric values, finding out the best set of tunes values is itself an optimization problem. The present study proposed a novel technique for parameter optimization of LPA-ICI filter using genetic algorithm (GA) for brain MR images de-noising. The experimental results proved that the proposed method outperforms the LPA-ICI method for de-noising in terms of various performance metrics for different noise variance levels. Obtained results reports that the ICI parameter values depend on the noise variance and the concerned under test image. View Full-Text
Keywords: magnetic resonance imaging; image de-noising; local polynomial approximation filter; optimization algorithms; genetic algorithm magnetic resonance imaging; image de-noising; local polynomial approximation filter; optimization algorithms; genetic algorithm
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MDPI and ACS Style

Dey, N.; Ashour, A.S.; Beagum, S.; Pistola, D.S.; Gospodinov, M.; Gospodinova, Е.P.; Tavares, J.M.R.S. Parameter Optimization for Local Polynomial Approximation based Intersection Confidence Interval Filter Using Genetic Algorithm: An Application for Brain MRI Image De-Noising. J. Imaging 2015, 1, 60-84. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging1010060

AMA Style

Dey N, Ashour AS, Beagum S, Pistola DS, Gospodinov M, Gospodinova ЕP, Tavares JMRS. Parameter Optimization for Local Polynomial Approximation based Intersection Confidence Interval Filter Using Genetic Algorithm: An Application for Brain MRI Image De-Noising. Journal of Imaging. 2015; 1(1):60-84. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging1010060

Chicago/Turabian Style

Dey, Nilanjan; Ashour, Amira S.; Beagum, Samsad; Pistola, Dimitra S.; Gospodinov, Mitko; Gospodinova, Еvgeniya P.; Tavares, João M.R.S. 2015. "Parameter Optimization for Local Polynomial Approximation based Intersection Confidence Interval Filter Using Genetic Algorithm: An Application for Brain MRI Image De-Noising" J. Imaging 1, no. 1: 60-84. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging1010060

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