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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
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)
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)
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)
Dougherty, G.: Digital Image Processing for Medical Applications. Cambridge University Press, Cambridge (2009)
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)
Ghosh, A.: Use of fuzziness measures in layered networks for object extraction: a generalization. Fuzzy Sets Syst. 72(3), 331–348 (1995)
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)
Gurney, K.: An Introduction to Neural Networks. CRC Press, London (2014)
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)
Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets, and classification. IEEE Trans. Neural Netw. 3(5), 683–697 (1992)
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)
Gonzalez, R.C., Woods, R.: Digital Image Processing. Pearson Education, London (2002)
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_34
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Morales-Lopez, H.I., Cruz-Vega, I., Ramirez-Cortes, J.M., Peregrina-Barreto, H., Rangel-Magdaleno, J. (2018). SOM-Like Neural Network and Differential Evolution for Multi-level Image Segmentation and Classification in Slit-Lamp Images. In: Orjuela-Cañón, A., Figueroa-García, J., Arias-Londoño, J. (eds) Applications of Computational Intelligence. ColCACI 2018. Communications in Computer and Information Science, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-03023-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-03023-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03022-3
Online ISBN: 978-3-030-03023-0
eBook Packages: Computer ScienceComputer Science (R0)