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A Bio-inspired Parallel-Framework Based Multi-gene Genetic Programming Approach to Denoise Biomedical Images

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Abstract

The occurrence of noise is a common problem in biomedical imaging applications. The denoising of corrupted biomedical images is a challenging task. In this paper, we present a biologically inspired parallel-framework based multi-gene genetic programming (MGGP) approach for denoising biomedical images from mixed impulse noise. Our biologically inspired approach has achieved an improved denoising performance by exploiting its parallel framework of multiple genes modeling capability in noise detection and removal stages. In the detection stage, we developed MGGP-based noise detector using rank-ordered and robust statistical features to effectively locate the corrupted pixels and generate noise map. In the noise removal stage, the detected noisy pixels are denoised by developing a bio-inspired MGGP-based estimator using statistical features of only noise-free pixels in their neighborhood. Extensive experimentation is carried out to demonstrate the robust performance of the proposed approach on diverse types of biomedical images corrupted with different noise densities. As a test case, we evaluated the performance of the proposed bio-inspired approach for benchmark biomedical images of Algae, C05c, Celulas, Crm04280, Crm05210, Nemacb1, Nemacl2, MRI, X-ray, Heart and microscopic images of fungal spores causing wheat rust. The proposed parallel-framework based bio-inspired approach has demonstrated an improved performance over other existing conventional and bio-inspired learning approaches.

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Acknowledgments

This work was supported by Higher Education Commission, Government of Pakistan under Indigenous PhD Fellowship Program-Batch VII, PIN No. 117-3250-EG7-012. The authors are also grateful to Dr. Dominic Searson for providing help regarding GPTIPS Toolbox. The authors are also thankful to National Institute of Lasers and Optronics (NILOP), Islamabad, Pakistan, for the provision of microscopic images of fungal spore’s germination.

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Authors and Affiliations

  1. Biomedical Informatics Research Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, P.O. Nilore, Islamabad, Pakistan

    Syed Gibran Javed, Abdul Majid, Safdar Ali & Nabeela Kausar

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  1. Syed Gibran Javed
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  2. Abdul Majid
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  3. Safdar Ali
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  4. Nabeela Kausar
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Correspondence to Syed Gibran Javed.

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Syed Gibran Javed, Abdul Majid, Safdar Ali and Nabeela Kausar declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Javed, S.G., Majid, A., Ali, S. et al. A Bio-inspired Parallel-Framework Based Multi-gene Genetic Programming Approach to Denoise Biomedical Images. Cogn Comput 8, 776–793 (2016). https://doi.org/10.1007/s12559-016-9416-6

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