Rician Noise Reduction by Combining Mathematical Morphological Operators through Genetic Programming

We propose a genetic programming (GP)-based approach for noise reduction from magnetic resonance imaging (MRI). An optimal composite morphological supervised filter ( F ocmsf ) is developed through a certain number of generations by combining gray-scale mathematical morphological (MM) operators unde...

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Vydáno v:Optical review Ročník 20; číslo 4; s. 289 - 292
Hlavní autoři: Sharif, Muhammad, Jaffar, Muhammad Arfan, Mahmood, Muhammad Tariq
Médium: Journal Article
Jazyk:angličtina
Vydáno: Tokyo Springer Japan 01.07.2013
Témata:
Atomic
genetic programming
image denoising
Lasers
Letter
mathematical morphology
Microwaves
Molecular
Optical and Plasma Physics
Optical Devices
Optics
Photonics
Physics
Physics and Astronomy
Quantum Optics
RF and Optical Engineering
Rician noise
mathematical morphology
genetic programming
Rician noise
image denoising
ISSN:1340-6000, 1349-9432
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Shrnutí:We propose a genetic programming (GP)-based approach for noise reduction from magnetic resonance imaging (MRI). An optimal composite morphological supervised filter ( F ocmsf ) is developed through a certain number of generations by combining gray-scale mathematical morphological (MM) operators under a fitness criterion. The proposed method does not need any prior information about the noise variance. The improved performance of the developed filter is investigated using simulated and real MRI datasets. Comparative analysis demonstrates the superiority of the proposed GP-based scheme over the existing approaches.
ISSN:1340-6000
1349-9432
DOI:10.1007/s10043-013-0052-z