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Neural Computing and Applications

, Volume 29, Issue 12, pp 1517–1533 | Cite as

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

  • Amira S. AshourEmail author
  • Samsad Beagum
  • Nilanjan Dey
  • Ahmed S. Ashour
  • Dimitra Sifaki Pistolla
  • Gia Nhu Nguyen
  • Dac-Nhuong Le
  • Fuqian Shi
Original Article

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.

Keywords

Image de-noising Microscopic imaging Poisson noise Local polynomial approximation filter Genetic algorithm Particle swarm optimization 

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Amira S. Ashour
    • 1
    Email author
  • Samsad Beagum
    • 2
    • 3
  • Nilanjan Dey
    • 4
  • Ahmed S. Ashour
    • 5
  • Dimitra Sifaki Pistolla
    • 6
  • Gia Nhu Nguyen
    • 7
  • Dac-Nhuong Le
    • 8
  • Fuqian Shi
    • 9
  1. 1.Department of Electronics and Electrical Communications Engineering, Faculty of EngineeringTanta UniversityTantaEgypt
  2. 2.College of Computers and Information TechnologyTaif UniversityTaifKingdom of Saudi Arabia
  3. 3.Karpagam UniversityCoimbatoreIndia
  4. 4.Department of Information TechnologyTechno India College of TechnologyKolkataIndia
  5. 5.Department of Human Anatomy and Embryology, Faculty of MedicineTanta UniversityTantaEgypt
  6. 6.Department of Social Medicine, Faculty of MedicineUniversity of CreteCreteGreece
  7. 7.Duy Tan UniversityDanangVietnam
  8. 8.Haiphong UniversityHaiphongVietnam
  9. 9.College of Information and EngineeringWenzhou Medical UniversityWenzhouPeople’s Republic of China

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