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  3. Volume 9, Number 4, 2022
  4. Hybridization of Divide-and-Conquer technique and Neural Network algorithm for better contrast enhancement in medical images

Hybridization of Divide-and-Conquer technique and Neural Network algorithm for better contrast enhancement in medical images

MMC.
2022;
Volume 9, Number 4
: pp. 921–935
https://doi.org/10.23939/mmc2022.04.921
Received: June 10, 2022
Accepted: September 17, 2022

Mathematical Modeling and Computing, Vol. 9, No. 4, pp. 921–935 (2022)

Authors:
  1. F. Aqel
  2. K. Alaa
  3. N. E. Alaa
  4. M. Atounti
1
IR2M Laboratory, Faculty of Sciences and Technics, Hassan First University
2
Laboratory of Applied Mathematics and Information Systems, Multidisciplinary Faculty of Nador, University of Mohammed First
3
Laboratory LAMAI, Faculty of Science and Technology Cadi Ayyad University
4
Laboratory of Applied Mathematics and Information Systems, Multidisciplinary Faculty of Nador, University of Mohammed First

The aim of this work is to propose a new method for optimal contrast enhancement of a medical image.  The main idea is to improve the Divide-and-Conquer method to enhance the contrast, and highlight the information and details of the image, based on a new conception of the Neural Network algorithm.  The Divide-and-Conquer technique is a suitable method for contrast enhancement with an efficiency that directly depends on the choice of weights in the decomposition subspaces.  A new hybrid algorithm was used for the optimal selection of weights, considering the optimization of the enhancement measure (EME).  To evaluate the proposed model's effectiveness, experimental results were presented showing that the proposed hybrid technique is robustly effective and produces clear and high contrast images.

medical images
contrast enhancement
Divide-and-Conquer technique
Neural Network Algorithm
optimization algorithm
Gray–Scott mathematical model
  1. Weickert J.  Anisotropic Diffusion in Image Processing.  Teubner–Verlag, Stuttgart (1998).
  2. Gonzalez R. C., Woods R. E.  Digital Image Processing.  Pearson Prenctice Hall (2007).
  3. Alaa N. E., Zirhem M.  Bio-inspired reaction diffusion system applied to image restoration.  International Journal of Bio-Inspired Computation.  12 (2), 128–137 (2018).
  4. Zhang Y., Gong S., Luo M.  Image quality guided biology application for genetic analysis.  Journal of Visual Communication and Image Representation.  64, 102606 (2019).
  5. Algazi V. R., Ford G. E., Hildum E.  Digital representation and storage of high quality color images by anisotropic enhancement and subsampling.  International Conference on Acoustics, Speech, and Signal Processing (1989).
  6. Goel N., Yadav A., Singh B. M.  Medical image processing: A review.  2016 Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH). 57–62 (2016).
  7. Deserno T. M.  Fundamentals of biomedical image processing.  In: Deserno T. (eds) Biomedical Image Processing. Biological and Medical Physics, Biomedical Engineering.  Springer, Berlin, Heidelberg (2010).
  8. Morris P. G.  Nuclear magnetic resonance imaging in medicine and biology (1986).
  9. Stark D. D., Bradley W. G.  Magnetic resonance imaging.  St. Louis, MO: Mosby (1992).
  10. Honeyman-Buck J.  Deserno T. M. (ed) Biomedical image processing.  Journal of Digital Imaging.  25, 689–691 (2012).
  11. Defrise M., Gullberg G. T.  Image reconstruction.  Physics in Medicine & Biology.  51 (13), R139 (2006).
  12. Reader A. J., Zaidi H.  Advances in PET image reconstruction.  PET Clinics.  2 (2), 173–190 (2007).
  13. Vasilenko G. I., Taratorin A. M.  Image reconstruction.  Moscow, Izdatel Radio Sviaz (1986).
  14. Lu L., Zheng Y., Carneiro G., Yang L.  Deep learning and convolutional neural networks for medical image computing.  Advances in Computer Vision and Pattern Recognition (2017).
  15. Analoui M.  Radiographic image enhancement. Part I: spatial domain techniques.  Dentomaxillofacial Radiology.  30 (1), 1–9 (2001).
  16. Rahman S., Rahman M. M., Hussain K., Khaled S. M., Shoyaib M.  Image enhancement in spatial domain: A comprehensive study.  2014 17th International Conference on Computer and Information Technology (ICCIT). 368–373 (2014).
  17. Agaian S. S., Panetta K., Grigoryan A. M.  A new measure of image enhancement.  In: IASTED International Conference on Signal Processing & Communication (pp. 19–22). (2000).
  18. Dhawan A. P.  Medical image analysis.  John Wiley & Sons. Vol. 31 (2011).
  19. Koehring A., Foo J. L., Miyano G., Lobe T., Winer E.  A framework for interactive visualization of digital medical images.  Journal of Laparoendoscopic & Advanced Surgical Techniques.  18 (5), 697–706 (2008).
  20. Silva S., Santos B. S., Madeira J., Silva A.  Processing, visualization and analysis of medical images of the heart: an example of fast prototyping using MeVisLab.  2009 Second International Conference in Visualisation. 165–170 (2009).
  21. Deserno T.  Medical image processing.  Optipedia, SPIE Press, Bellingham, WA (2009).
  22. Marion J. L.  Analysis of justification for modality integration.  Proc. SPIE 0418, Picture Archiving and Communication Systems.  418, 17–23 (1983).
  23. Land E. H.  The retinex.  American Scientist.  52 (2), 247–264 (1964).
  24. Land E. H., McCann J. J.  Lightness and retinex theory.  Journal of the Optical Society of America.  61 (1), 1–11 (1971).
  25. Fu X., Sun Y., LiWang M., Huang Y., Zhang X.-P., Ding X.  A novel retinex based approach for image enhancement with illumination adjustment.  2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1190–1194 (2014).
  26. Ng M. K., Wang W.  A total variation model for Retinex.  SIAM Journal on Imaging Sciences.  4 (1), 345–365 (2011).
  27. Wang L., Xiao L., Liu H., Wei Z.  Variational Bayesian method for retinex.  IEEE Transactions on Image Processing.  23 (8), 3381–3396 (2014).
  28. Polesel A., Ramponi G., Mathews V. J.  Image enhancement via adaptive unsharp masking.  IEEE transactions on image processing.  9 (3), 505–510 (2000).
  29. Deng G.  A generalized unsharp masking algorithm.  IEEE transactions on Image Processing.  20 (5), 1249–1261 (2010).
  30. Wang Y., Chen Q., Zhang B.  Image enhancement based on equal area dualistic sub-image histogram equalization method.  IEEE transactions on Consumer Electronics.  45 (1), 68–75 (1999).
  31. Patel S., Goswami M.  Comparative analysis of Histogram Equalization techniques.  2014 International Conference on Contemporary Computing and Informatics (IC3I). 167–168 (2014).
  32. Kim J. Y., Kim L. S., Hwang S. H.  An advanced contrast enhancement using partially overlapped sub-block histogram equalization.  IEEE transactions on circuits and systems for video technology.  11 (4), 475–484 (2001).
  33. Bacquey N.  The packing problem: A divide and conquer algorithm on cellular automata.  Automata & JAC. 1–10 (2012).
  34. Stout Q. F.  Supporting divide-and-conquer algorithms for image processing.  Journal of Parallel and Distributed Computing.  4 (1), 95–115 (1987).
  35. Zhuang P., Fu X., Huang Y., Ding X.  Image enhancement using divide-and-conquer strategy.  Journal of Visual Communication and Image Representation.  45, 137–146 (2017).
  36. Alaa K., Atounti M., Zirhem M.  Image restoration and contrast enhancement based on a nonlinear reaction-diffusion mathematical model and divide & conquer technique.  Mathematical Modeling and Computing.  8 (3), 549–559 (2021).
  37. Zhuang P., Ding X.  Divide-and-conquer framework for image restoration and enhancement.  Engineering Applications of Artificial Intelligence.  85, 830–844 (2019).
  38. Liu D. N., Hou R., Wu W. Z., Hua J. W., Wang X. Y., Pang B.  Research on infrared image enhancement and segmentation of power equipment based on partial differential equation.  Journal of Visual Communication and Image Representation.  64, 102610 (2019).
  39. Gray P., Scott S. K.  Autocatalytic reactions in the isothermal, continuous stirred tank reactor: Isolas and other forms of multistability.  Chemical Engineering Science.  38 (1), 29–43 (1983).
  40. McGough J. S., Riley K.  Pattern formation in the Gray–Scott model.  Nonlinear Analysis: Real World Applications.  5 (1), 105–121 (2004).
  41. Alaa H., Alaa N. E., Aqel F., Lefraich H.  A new Lattice Boltzmann method for a Gray–Scott based model applied to image restoration and contrast enhancement.  Mathematical Modeling and Computing.  9 (2), 187–202 (2022).
  42. Li W., Shi M., Ogunbona P.  A new divide and conquer algorithm for graph-based image and video segmentation.  2005 IEEE 7th Workshop on Multimedia Signal Processing. 1–4 (2005).
  43. Körting T. S., Castejon E. F., Fonseca L. M. G.  The divide and segment method for parallel image segmentation.  International Conference on Advanced Concepts for Intelligent Vision Systems. 504–515 (2013).
  44. Zirhem M., Alaa N. E.  Texture synthesis by reaction diffusion process.  Annals of the University of Craiova-Mathematics and Computer Science Series.  42 (1), 56–69 (2015).
  45. Alaa N., Zirhem M.  Entropy solution for a fourth-order nonlinear degenerate problem for image decomposition.  J. Adv. Math. Stud.  11, 412–427 (2018).
  46. Sadollah A., Sayyaadi H., Yadav A.  A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm.  Applied Soft Computing.  71, 747–782 (2018).
  47. Parizeau M.  Réseaux de neurones.  GIF-21140 et GIF-64326, 124 (2004).
  48. Dreyfus G.  Réseaux de neurones: Méthodologie et applications.  Eyrolles (2004).