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


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.


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


  1. 1.
    Lee K, Kim K, Jung J, Heo J, Cho S, Lee S, Chang G, Jo Y, Park H, Park Y (2013) Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications. Sensors 13:4170–4191CrossRefGoogle Scholar
  2. 2.
    Timpson P, McGhee E, Anderson K (2011) Imaging molecular dynamics in vivo—from cell biology to animal models. J Cell Sci 124(17):2877–2890CrossRefGoogle Scholar
  3. 3.
    Mavandadi S, Dimitrov S, Feng S, Yu F, Sikora U, Lidere O, Padmanabhan S, Nielsen K, Ozcan A (2012) Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study. PLoS ONE 7(5):e37245. doi: 10.1371/journal.pone.0037245 CrossRefGoogle Scholar
  4. 4.
    Dey N, Ashour AS, Ashour AS, Singh A (2015) Digital analysis of microscopic images in medicine. J Adv Microsc Res 10:1–13CrossRefGoogle Scholar
  5. 5.
    Hore S, Chakroborty S, Ashour AS, Dey N, Ashour AS, Sifaki-pistolla D, Bhattacharya T, Chaudhuri SRB (2015) Finding contours of hippocampus brain cell using microscopic image analysis. J Adv Microsc Res 10(2):93–103. doi: 10.1166/jamr.2015.1245
  6. 6.
    Murphy DB (2001) Fundamentals of light microscopy and electronic imaging. Wiley, New YorkGoogle Scholar
  7. 7.
    Katkovnik V, Egiazarian K, Astola J (2002) Adaptive window size image de-noising based on intersection of confidence intervals (ICI) rule. J Math Imaging Vis 16:223–235MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Hu Y, Jiang X, Xin F, Zhang T, Yuan J, Zhai L, Guo C (2008) An algorithm on processing medical image based on rough-set and genetic algorithm. In: International conference on information technology and applications in biomedicine, (2008.ITAB 2008), pp 109–111Google Scholar
  9. 9.
    Samanta S, Dey N, Das P, Acharjee S, Chaudhuri S (2012) Multilevel threshold based gray scale image segmentation using cuckoo search. In: International conference on emerging trends in electrical, communication and information technologies (ICECIT)Google Scholar
  10. 10.
    Chakraborty S, Pal A, Dey N, Das D, Acharjee S (2014) Foliage area computation using monarch butterfly algorithm. In: 2014 International conference on non conventional energy (ICONCE 2014)Google Scholar
  11. 11.
    Acharjee S, Dey N, Samanta S, Das D, Roy R, Chakraborty S, Chaudhuri S (2014) ECG signal compression using ant weight lifting algorithm for tele-monitoring. J Med Imaging Health InformGoogle Scholar
  12. 12.
    Day N, Samanta S, Chakraborty S, Das A, Chaudhuri S, Suri J (2014) Firefly algorithm for optimization of scaling factors during embedding of manifold medical information: an application in ophthalmology imaging. J Med Imaging Health Inform 4(3):384–394CrossRefGoogle Scholar
  13. 13.
    Bai Q (2010) Analysis of particle swarm optimization algorithm. Comput Inf Sci 3(1):180–184Google Scholar
  14. 14.
    George G, Raimond K (2013) A survey on optimization algorithms for optimizing the numerical functions. Int J Comput Appl 61(6):41–46Google Scholar
  15. 15.
    Willett RM, Nowak RD (2004) Fast multiresolution photon-limited image reconstruction. In: IEEE international symposium on biomedical imaging: nano to macro, vol 2, pp 1192–1195Google Scholar
  16. 16.
    Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3d-transform domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rodrigues I, Sanches J (2009) Fluorescence microscopy imaging denoising with log-Euclidean priors and photobleaching compensation.In: 16th IEEE international conference on image processing (ICIP), pp 809–812Google Scholar
  18. 18.
    Homem MRP, Zorzan MR, Mascarenhas NDA (2011) Poisson noise reduction in deconvolution microscopy. J Comput Interdiscip Sci 2(3):173–177Google Scholar
  19. 19.
    Luisier F, Blu T, Unser M (2011) Image denoising in mixed Poisson–Gaussian noise. IEEE Trans Image Process 20(3):696–708MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Jezierska A, Talbot H, Chaux C, Pesquet J, Engler G (2012) Poisson–Gaussian noise parameter estimation in fluorescence microscopy imaging. In: International symposium on biomedical imaging (ISBI), BarcelonaGoogle Scholar
  21. 21.
    Katkovnik V (2005) Multiresolution local polynomial regression: a new approach to pointwise spatial adaptation. Digit Sig Process 15:73–116CrossRefGoogle Scholar
  22. 22.
    Katkovnik V, Egiazarian K, Astola J (2005) A spatially adaptive nonparametric regression image deblurring. IEEE Trans Image Process 14(10):1469–1478CrossRefGoogle Scholar
  23. 23.
    Ercole C, Foiá A, Katkovnik V, Egiazaria K (2005) Spatio-temporal pointwise adaptive denoising of video: 3d non-parametric regression approach. Workshop on video processing and quality metrics for consumer electronicsGoogle Scholar
  24. 24.
    Tan X, Sun C, Pham TD (2014) Multipoint filtering with local polynomial approximation and range guidance. In: CVPR ‘14: proceedings of the 2014 IEEE conference on computer vision and pattern recognition, pp 2941–2948Google Scholar
  25. 25.
    Misra D, Sarker S, Dhabal S, Ganguly A (2013) Effect of using genetic algorithm to denoise MRI images corrupted with Rician noise. in: 2013 IEEE international conference on emerging trends in computing, communication and nanotechnology (ICECCN 2013), pp 146–151Google Scholar
  26. 26.
    Liu Y, Ma Y, Liu F, Zhang X, Yang Y (2014) The research based on the genetic algorithm of wavelet image denoising threshold of medicine. J Chem Pharm Res 6:2458–2462Google Scholar
  27. 27.
    Kumar M, Mishra SK (2015) Particle swarm optimization-based functional link artificial neural network for medical image denoising. Computational vision and robotics. Springer India, pp 105–111Google Scholar
  28. 28.
    Boyat AK, Joshi BK (2015) A review paper: noise models in digital image processing. Sig Image Process 6(2):63–75Google Scholar
  29. 29.
    Foi A, Trimeche M, Katkovnik V, Egiazarian K (2008) Practical Poissonian–Gaussian noise modeling and fitting for singleimage raw-data. IEEE Trans Image Process 17(10):1737–1754MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Hasinoff SW, Durand F, Freeman WT (2010) Noise-optimal capture for high dynamic range photography. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 553–560Google Scholar
  31. 31.
    Dey N, Karâa WB (2015) Biomedical Image analysis and mining techniques for improved health outcomes. Advances in bioinformatics and biomedical engineering (ABBE) book series, 414 p. doi: 10.4018/978-1-4666-8811-7
  32. 32.
    Dey N, Nandi B, Roy AB, Biswas D, Das A, Chaudhuri S (2013) Analysis of blood cell smears using stationary wavelet transform & harris corner detection. recent advances in computer vision and image processing: methodologies and applications, pp 357–370Google Scholar
  33. 33.
    N Dey, B Nandi, P Das, A Das, SS Chaudhuri, Retention Of Electrocardiogram Features Insignificantly Devalorized as an Effect of Watermarking for a Multi-Modal Biometric Authentication System. Advances in Biometrics for Secure Human Authentication and Recognition, 1-450 (2013)Google Scholar
  34. 34.
    Dey N, Samanta S, Yang XS, Chaudhri S, Das A (2013) Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search. Int J Bio-Inspir Comput 5(5):315–326CrossRefGoogle Scholar
  35. 35.
    Dey N, Roy A, Pal M, Das A (2012) FCM based blood vessel segmentation method for retinal images. Int J Comput Sci Netw 1(3):1–5Google Scholar
  36. 36.
    Dey N, Pal M, Das A (2012) A session based watermarking technique within the NROI of retinal fundus images for authencation using DWT, spread spectrum and Harris corner detection. Int J Mod Eng Res 2(3):749–757Google Scholar
  37. 37.
    Roy P, Goswami S, Chakraborty S, Azar AT, Dey N (2014) Image segmentation using rough set theory: a review. Int J Rough Sets Data Anal 1(2):62–74CrossRefGoogle Scholar
  38. 38.
    Nandi D, Ashour AS, Samanta S, Chakraborty S, Salem M, Dey N (2015) Principal component analysis in medical image processing: a study. Int J Image Mining 1(1):65–86CrossRefGoogle Scholar
  39. 39.
    Ashour AS, Samanta S, Dey N, Kausar N, Karaa WB, Hassanien AE (2015) Computed tomography image enhancement using Cuckoo search: a log transform based approach. J Sig Inform Process 6(4):244–257CrossRefGoogle Scholar
  40. 40.
    Dey N, Das P, Biswas D, Maji P, Das A, Chaudhuri SS (2013) Visible watermarking within the region of non-interest of medical images based on fuzzy C-means and Harris corner detection. The fourth international workshop communications security and information assurance (CSIA-2013), DelhiGoogle Scholar
  41. 41.
    Tran G, Shi Y (2015) Fiber orientation and compartment parameter estimation from multi-shell diffusion imaging. IEEE Trans Med Imaging 34(11):2320–2332CrossRefGoogle Scholar
  42. 42.
    Samanta S, Dey N, Das P, Acharjee S, Chaudhuri SS (2012) Multilevel threshold based gray scale image segmentation using Cuckoo search. In: International conference on emerging trends in electrical, communication and information technologies-ICECITGoogle Scholar
  43. 43.
    Samanta S, Chakraborty S, Acharjee S, Mukherjee A, Dey N (2013) Solving 0/1 knapsack problem using ant weight lifting algorithm. In: 2013 IEEE international conference on computational intelligence and computing research (ICCIC), MaduraiGoogle Scholar
  44. 44.
    Chakraborty S, Samanta S, Mukherjee A, Dey N, Chaudhuri SS (2013) Particle swarm optimization based parameter optimization technique in medical information hiding. In: 2013 IEEE international conference on computational intelligence and computing research (ICCIC), MaduraiGoogle Scholar
  45. 45.
    Gaber T, Kotyk T, Dey N, Ashour A, Victoria ADC, Hassanien AE, Snasel V (2015) Detection of dead stained microscopic cells based on color intensity and contrast. In: International conference on advanced intelligent systems and informatics. BeniSuef University, BeniSuefGoogle Scholar
  46. 46.
    Dey N, Das P, Roy AB, Das A, Chaudhuri SS (2012) Detection and measurement of arc of lumen calcification from intravascular ultrasound using Harris corner detection. In: National conference on computing and communication systems (NCCCS), DurgapurGoogle Scholar
  47. 47.
    Dey N, Ashour AS, Beagum S, Sifaki-Pistola D, Gospodinov M, Gospodinova EP, Tavares JMRS (2015) Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising. J Imaging 1:60–84CrossRefGoogle Scholar
  48. 48.
    Wand MP, Jones MC (1995) Kernel smoothing. Monographs on statistics and applied probability. Chapman and Hall, LondonGoogle Scholar
  49. 49.
    Ashour A, Elkamchouchi H (2007) Enhancement of moving targets tracking performance using the ICI rule. Alex Eng J 46:673–682Google Scholar
  50. 50.
    Katkovnik V, Egiazarian K, Astola J (2006) Local approximation techniques in signal and image processing. SPIE, BellinghamCrossRefzbMATHGoogle Scholar
  51. 51.
    Toledo CFM, Oliveira L, Silva RD, Pedrini H (2013) Image denoising based on genetic algorithm. In: 2013 IEEE congress on evolutionary computation (CEC), pp 1294–1301Google Scholar

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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