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Light microscopy image de-noising using optimized LPA-ICI filter

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Abstract

Microscopic images are often corrupted by noise, where Poisson noise is one of the major types that can damage them. The local polynomial approximation (LPA) filter supported by the intersection confidence interval (ICI) rule was considered as an efficient filter for image de-noising. However, this filter depends on several parameters that affect its performance. In order to determine the optimal parameters, the present study employed the classic LPA-ICI (C-LPA-ICI) filter supported by optimization algorithms, namely the genetic algorithm (GA) and the particle swarm optimization (PSO) in the context of light microscopy imaging systems. Nevertheless, inclusion of the optimization algorithms increased the computational time. A novel automatic technique entitled “Standard Optimized LPA-ICI” (SO-LPA-ICI) is proposed. In this context, the average of the optimized ICI parameters was calculated, which obtained from both LPA-ICI-based GA (G-LPA-ICI) and LPA-ICI-based PSO (P-LPA-ICI). Thus, the proposed SO-LPA-ICI is included the optimal ICI parameters without optimization iterations. This procedure is proposed to speed up the optimized filter. A pool of 50 rats’ renal microscopic images is involved to test the proposed approach. A comparative study was conducted to compare the effectiveness of the four methods, namely C-LPA-ICI, G-LPA-ICI, P-LPA-ICI, and the SO-LPA-ICI for de-noising in the presence of Poisson noise. The experimental results established the outstanding performance of the SO-LPA-ICI in terms of the PSNR, MAE, and MSSIM with 28.27, 7.65, and 0.93 values, respectively. In addition, the proposed approach achieved fast de-noising compared to the G-LPA-ICI and the P-LPA-ICI.

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Authors and Affiliations

  1. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  2. College of Computers and Information Technology, Taif University, Taif, Kingdom of Saudi Arabia

    Samsad Beagum

  3. Karpagam University, Coimbatore, Tamil Nadu, India

    Samsad Beagum

  4. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

  5. Department of Human Anatomy and Embryology, Faculty of Medicine, Tanta University, Tanta, Egypt

    Ahmed S. Ashour

  6. Department of Social Medicine, Faculty of Medicine, University of Crete, Crete, Greece

    Dimitra Sifaki Pistolla

  7. Duy Tan University, Danang, Vietnam

    Gia Nhu Nguyen

  8. Haiphong University, Haiphong, Vietnam

    Dac-Nhuong Le

  9. College of Information and Engineering, Wenzhou Medical University, 325035, Wenzhou, People’s Republic of China

    Fuqian Shi

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  1. Amira S. Ashour
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  2. Samsad Beagum
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  8. Fuqian Shi
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Correspondence to Amira S. Ashour.

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Ashour, A.S., Beagum, S., Dey, N. et al. Light microscopy image de-noising using optimized LPA-ICI filter. Neural Comput & Applic 29, 1517–1533 (2018). https://doi.org/10.1007/s00521-016-2678-9

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  • DOI: https://doi.org/10.1007/s00521-016-2678-9

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