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SOM-Like Neural Network and Differential Evolution for Multi-level Image Segmentation and Classification in Slit-Lamp Images

  • Hans Israel Morales-LopezEmail author
  • Israel Cruz-Vega
  • Juan Manuel Ramirez-Cortes
  • Hayde Peregrina-Barreto
  • Jose Rangel-Magdaleno
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 833)

Abstract

A nuclear cataract is a type of disease of the eye that affects a considerable part of the human population at an advanced age. Due to the high demand for clinical services, computer algorithms based on artificial intelligence have emerged, providing acceptable aided diagnostics to the medical field. However, several challenges are yet to be overcome. For instance, a well-segmented image of the region of interest could prove valuable at a previous stage in the automatic classification of this disease. A great variety of research in image classification uses several image processing techniques before the classification stage. In this paper, we explore the automatic segmentation based on two leading techniques, namely, a Self-Organizing Multilayer (SOM) Neural Network (NN) and Differential Evolution (DE) methods. Specifically, the fuzzy entropy measure used here is optimized via a neural process, and by using the evolutive technique, optimal thresholds of the images are obtained. The experimental part shows significant results in getting a useful automatic segmentation of the medical images. In this extended version, we have implemented the use of a Multilayer Perceptron, a classifier that proves the usefulness of the segmented images.

Keywords

SOM Neural network Image segmentation Differential evolution Multilayer perceptron Classification 

References

  1. 1.
    Aizenberg, I., Aizenberg, N., Hiltner, J., Moraga, C., Zu Bexten, E.M.: Cellular neural networks and computational intelligence in medical image processing. Image Vis. Comput. 19(4), 177–183 (2001)CrossRefGoogle Scholar
  2. 2.
    Awad, M., Chehdi, K., Nasri, A.: Multicomponent image segmentation using a genetic algorithm and artificial neural network. IEEE Geosci. Remote. Sens. Lett. 4(4), 571–575 (2007)CrossRefGoogle Scholar
  3. 3.
    Boskovitz, V., Guterman, H.: An adaptive neuro-fuzzy system for automatic image segmentation and edge detection. IEEE Trans. Fuzzy Syst. 10(2), 247–262 (2002)CrossRefGoogle Scholar
  4. 4.
    De Luca, A., Termini, S.: A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Inf. Control. 20(4), 301–312 (1972)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Dougherty, G.: Digital Image Processing for Medical Applications. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  6. 6.
    Gacsádi, A., Szolgay, P.: Variational computing based segmentation methods for medical imaging by using CNN. In: 2010 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA), pp. 1–6. IEEE (2010)Google Scholar
  7. 7.
    Ghosh, A.: Use of fuzziness measures in layered networks for object extraction: a generalization. Fuzzy Sets Syst. 72(3), 331–348 (1995)CrossRefGoogle Scholar
  8. 8.
    Ghosh, A., Pal, N.R., Pal, S.K.: Self-organization for object extraction using a multilayer neural network and fuzziness mearsures. IEEE Trans. Fuzzy Syst. 1(1), 54–68 (1993)CrossRefGoogle Scholar
  9. 9.
    Gurney, K.: An Introduction to Neural Networks. CRC Press, London (2014)CrossRefGoogle Scholar
  10. 10.
    Lin, J.-S., Cheng, K.-S., Mao, C.-W.: A fuzzy hopfield neural network for medical image segmentation. IEEE Trans. Nucl. Sci. 43(4), 2389–2398 (1996)CrossRefGoogle Scholar
  11. 11.
    Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets, and classification. IEEE Trans. Neural Netw. 3(5), 683–697 (1992)CrossRefGoogle Scholar
  12. 12.
    Paul, S., Bandyopadhyay, B.: A novel approach for image compression based on multi-level image thresholding using shannon entropy and differential evolution. In: 2014 IEEE Students’ Technology Symposium (TechSym), pp. 56–61. IEEE (2014)Google Scholar
  13. 13.
    Gonzalez, R.C., Woods, R.: Digital Image Processing. Pearson Education, London (2002)Google Scholar
  14. 14.
    Sarkar, S., Paul, S., Burman, R., Das, S., Chaudhuri, S.S.: A fuzzy entropy based multi-level image thresholding using differential evolution. In: Panigrahi, B.K., Suganthan, P.N., Das, S. (eds.) SEMCCO 2014. LNCS, vol. 8947, pp. 386–395. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-20294-5_34CrossRefGoogle Scholar
  15. 15.
    Tao, W.-B., Tian, J.-W., Liu, J.: Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm. Pattern Recognit. Lett. 24(16), 3069–3078 (2003)CrossRefGoogle Scholar
  16. 16.
    Vilariño, D.L., Rekeczky, C.: Pixel-level snakes on the CNNUM: algorithm design, on-chip implementation and applications. Int. J. Circ. Theory Appl. 33(1), 17–51 (2005)CrossRefGoogle Scholar
  17. 17.
    Vilariño, D.L., Cabello, D., Pardo, X.M., Brea, V.M.: Cellular neural networks and active contours: a tool for image segmentation. Image Vis. Comput. 21(2), 189–204 (2003)CrossRefGoogle Scholar
  18. 18.
    Yin, S., Zhao, X., Wang, W., Gong, M.: Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization. Pattern Recognit. 47(9), 2894–2907 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hans Israel Morales-Lopez
    • 1
    Email author
  • Israel Cruz-Vega
    • 1
  • Juan Manuel Ramirez-Cortes
    • 1
  • Hayde Peregrina-Barreto
    • 1
  • Jose Rangel-Magdaleno
    • 1
  1. 1.Instituto Nacional de Astrofísica, Óptica y ElectrónicaPueblaMexico

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