Optimal composite morphological supervised filter for image denoising using genetic programming: Application to magnetic resonance images

Composite filters based on mathematical morphological operators (MMO) are getting considerable attraction in image denoising. Most of such approaches depend on pre-fixed combination of MMO. In this paper, we proposed a genetic programming (GP) based approach for denoising magnetic resonance images (...

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Vydáno v:Engineering applications of artificial intelligence Ročník 31; s. 78 - 89
Hlavní autoři: Sharif, Muhammad, Arfan Jaffar, Muhammad, Tariq Mahmood, Muhammad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2014
Témata:
Genetic programming
Genetics
Image restoration
Magnetic resonance
Magnetic resonance imaging
Mathematical morphology
Modules
Noise reduction
Optimization
Preprocessing
Programming
Rician noise
Image restoration
Magnetic resonance imaging
Rician noise
Mathematical morphology
Genetic programming
ISSN:0952-1976, 1873-6769
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Shrnutí:Composite filters based on mathematical morphological operators (MMO) are getting considerable attraction in image denoising. Most of such approaches depend on pre-fixed combination of MMO. In this paper, we proposed a genetic programming (GP) based approach for denoising magnetic resonance images (MRI) that evolves an optimal composite morphological supervised filter (FOCMSF) by combining the gray-scale MMO. The proposed method is divided into three modules: preprocessing module, GP module, and evaluation module. In preprocessing module, the required components for the development of the proposed GP based filter are prepared. In GP module, FOCMSF is evolved through evaluating the fitness of several individuals over certain number of generations. Finally, the evaluation module provides the mechanism for testing and evaluating the performance of the evolved filter. The proposed method does not need any prior information about the noise variance. The improved performance of the developed filter is investigated using the standard MRI datasets and its performance is compared with previously proposed methods. Comparative analysis demonstrates the superiority of the proposed GP based scheme over the existing approaches.
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ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2013.11.011