The novel self-organizing map combined with fuzzy C-means and K-means convolution for a soft and hard natural digital image segmentation

Document Type : Original Article


Department of mathematics and computer science of Islamic Azad University, Science and Research Branch, Tehran, Iran


Digital image segmentation plays an important role in noise reduction and pixel clustering for pre-processing of deep learning or feature extraction. The classic Self-Organizing Map (SOM) algorithm is a well-known unsupervised clustering neural network model. This classic method works on continuous data instead of discrete data sets with a widely scattered distribution. The novel SOM(SOM2) modelling solved this problem for the classic, simple tabular discrete data set but not for the digital image data. As the essence of digital image pixels data are different from tabular datasets, we have to look at them differently. This paper proposes exploiting the novel SOM method with a hybrid combination of the fuzzy C-Means and K-means convolution filter as image segmentation and noise reduction with soft and hard segmentation as entropy reduction for natural digital images. The main approach of this paper is the segmentation of image contents for the reduction of noises and saturation pixels by entropy criteria. Based on the resulting paper, the combination of SOM2 with FCM for soft segmentation 47%-and the combination of SOM2 with k-means convolution for hard segmentation 33% can reduce the entropy of the original image on average.


  1. Abdel-Basset, R. Mohamed, N. M. AbdelAziz, and M. Abouhawwash, HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation, Expert Systems with Applications, 190 (2022), p. 116145.
  2. Aghajari and G. D. Chandrashekhar, Self-organizing map based extended fuzzy C-means (SEEFC) algorithm for image segmentation, Applied Soft Computing, 54 (2017), pp. 347–363.
  3. Aouat, I. Ait-hammi, and I. Hamouchene, A new approach for texture segmentation based on the gray level co-occurrence matrix, Multimedia Tools and Applications, 80 (2021), pp. 24027–24052.
  4. Bigdeli, A. Maghsoudi, and R. Ghezelbash, Application of self-organizing map (SOM) and K-means clustering algorithms for portraying geochemical anomaly patterns in moalleman district, NE iran, Journal of Geochemical Exploration, 233 (2022), p. 106923.
  5. Borjigin and P. K. Sahoo, Color image segmentation based on multi-level Tsallis–Havrda–Charv´at entropy and 2D histogram using PSO algorithms, Pattern Recognition, 92 (2019), pp. 107–118.
  6. Bouwmans, A. Sobral, S. Javed, S. K. Jung, and E.-H. Zahzah, Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset, Computer Science Review, 23 (2017), pp. 1–71.
  7. Cheng, C. Cao, J. Yang, Z. Zhang, and Y. Chen, A spatially constrained skew student’s-t mixture model for brain MR image segmentation and bias field correction, Pattern Recognition, 128 (2022), p. 108658.
  8. Cuevas, D. Berjon, and N. Garc´ ´ıa, Grass band detection in soccer images for improved image registration, Signal Processing: Image Communication, 109 (2022), p. 116837.
  9. Dehghanian, S. S. M. Nadoushani, B. Saghafian, and R. Akhtari, Performance evaluation of a fuzzy hybrid clustering technique to identify flood source areas, Water Resources Management, 33 (2019), pp. 4621–4636.
  10. Gagliardi, A. Raffo, U. Fugacci, S. Biasotti, W. Rocchia, H. Huang, B. B. Amor, Y. Fang, Y. Zhang, X. Wang, et al., Shrec 2022: Protein–ligand binding site recognition, Computers & Graphics, 107 (2022), pp. 20–31.
  11. Ghaseminezhad and A. Karami, A novel self-organizing map (SOM) neural network for discrete groups of data clustering, Applied Soft Computing, 11 (2011), pp. 3771–3778.
  12. C. Gonzalez and R. E. Woods, Digital image processing, hoboken, NJ: Pearson, (2018).
  13. Guo and H. Peng, A novel multilevel color image segmentation technique based on an improved firefly algorithm and energy curve, Evolving Systems, (2022), pp. 1–49.
  14. R. Hait, R. Mesiar, P. Gupta, D. Guha, and D. Chakraborty, The bonferroni mean-type preaggregation operators construction and generalization: Application to edge detection, Information fusion, 80 (2022), pp. 226–240.
  15. Karami, S. Bohluli, C. Huang, and N. Sohaee, Deep learning model for express lane traffic forecasting, AUT Journal of Mathematics and Computing, 3 (2022), pp. 129–135.
  16. Kohonen and P. Somervuo, Self-organizing maps of symbol strings, Neurocomputing, 21 (1998), pp. 19– 30.
  17. Liang, Z. Cheng, H. Zhong, A. Qu, and L. Chen, A region-based convolutional network for nuclei detection and segmentation in microscopy images, Biomedical Signal Processing and Control, 71 (2022), p. 103276.
  18. Lu, S. Young, H. Wang, and N. Wijewardane, Robust plant segmentation of color images based on image contrast optimization, Computers and Electronics in Agriculture, 193 (2022), p. 106711.
  19. Mohammadi and M. I. Mobarakeh, An integrated clustering algorithm based on firefly algorithm and self-organized neural network, Progress in Artificial Intelligence, 11 (2022), pp. 207–217.
  20. Motta, L. Callea, L. Bonati, and A. Pandini, Pathdetect-SOM: A neural network approach for the identification of pathways in ligand binding simulations, Journal of Chemical Theory and Computation, 18 (2022), pp. 1957–1968. PMID: 35213804.
  21. Murawwat, H. M. Asif, S. Ijaz, M. I. Malik, and K. Raahemifar, Denoising and classification of arrhythmia using memd and ann, Alexandria Engineering Journal, 61 (2022), pp. 2807–2823.
  22. Nan, Y. Li, X. Jia, L. Dong, and Y. Chen, Application of improved som network in gene data cluster analysis, Measurement, 145 (2019), pp. 370–378.
  23. Nayak, B. Naik, and H. Behera, Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014, in Computational Intelligence in Data Mining-Volume 2: Proceedings of the International Conference on CIDM, 20-21 December 2014, Springer, 2015, pp. 133–149.
  24. Oliva, S. Hinojosa, V. Osuna-Enciso, E. Cuevas, M. Perez-Cisneros, and G. Sanchez-Ante´ , Image segmentation by minimum cross entropy using evolutionary methods, Soft Computing, 23 (2019), pp. 431–450.
  25. G. Oskouei, M. Hashemzadeh, B. Asheghi, and M. A. Balafar, Cgffcm: Cluster-weight and grouplocal feature-weight learning in fuzzy C-means clustering algorithm for color image segmentation, Applied Soft Computing, 113 (2021), p. 108005.
  26. T. Ouyang, S. K. Liao, Y. K. Gong, et al., Optimization of K-means image segmentation based on manta ray foraging algorithm, in 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), IEEE, 2022, pp. 151–155.
  27. Prezelj, J. Murovec, S. Huemer-Kals, K. Hasler, and P. Fischer¨ , Identification of different manifestations of nonlinear stick–slip phenomena during creep groan braking noise by using the unsupervised learning algorithms k-means and self-organizing map, Mechanical systems and signal processing, 166 (2022), p. 108349.
  28. Saberi, R. Sharbati, and B. Farzanegan, A gradient ascent algorithm based on possibilistic fuzzy C-means for clustering noisy data, Expert Systems with Applications, 191 (2022), p. 116153.
  29. G. Sodjinou, V. Mohammadi, A. T. S. Mahama, and P. Gouton, A deep semantic segmentationbased algorithm to segment crops and weeds in agronomic color images, Information Processing in Agriculture, 9 (2022), pp. 355–364.
  30. Song, Y. Liu, X. Zhang, Q. Wu, J. Gao, W. Wang, J. Li, Y. Song, and C. Yang, Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data, Nucleic acids research, 49 (2021), pp. e18–e18.
  31. Wang and S. Sun, A rock fabric classification method based on the grey level co-occurrence matrix and the gaussian mixture model, Journal of Natural Gas Science and Engineering, 104 (2022), p. 104627.
  32. Yang, J. Wu, J. Huo, Y.-K. Lai, and Y. Gao, Learning 3D face reconstruction from a single sketch, Graphical Models, 115 (2021), p. 101102.
  33. M. Zareian, M. Mesbah, S. Moradi, and M. Ghatee, A combined apriori algorithm and fuzzy controller for simultaneous ramp metering and variable speed limit determination in a freeway, AUT Journal of Mathematics and Computing, 3 (2022), pp. 237–251.
  34. Zhang, H. Li, N. Chen, S. Chen, and J. Liu, Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation, Pattern Recognition, 121 (2022), p. 108201.