A Genetic Fuzzy Rules Learning Approach for Unseeded Segmentation in Echography

  • Leonardo Bocchi
  • Francesco Rogai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)


Clinical practice in echotomography often requires effective and time-efficient procedures for segmenting anatomical structures to take medical decisions for therapy and diagnosis. In this work we present a methodology for image segmentation in echography with the aim to assist the clinician in these delicate tasks. A generic segmentation algorithm, based on region evaluation by means of a fuzzy rules based inference system (FRBS), is refined in a fully unseeded segmentation algorithm. Rules composing knowledge base are learned with a genetic algorithm, by comparing computed segmentation with human expert segmentation. Generalization capabilities of the approach are assessed with a larger test set and over different applications: breast lesions, ovarian follicles and anesthetic detection during brachial anesthesia.


Membership Function Cellular Automaton Fuzzy Rule Fuzzy Inference System Active Contour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baker, J.E.: Reducing Bias and Inefficiency in the Selection Algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms and their Application, pp. 14–21. Lawrence Erlbaum, Hillsdale (1987)Google Scholar
  2. 2.
    Bocchi, L., Rogai, F.: Segmentation of Ultrasound Breast Images: Optimization of Algorithm Parameters. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 163–172. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Caruana, R., Eshelman, L.: Representation and hidden bias II: Eliminating defining length bias in genetic search via shuffle crossover. In: Ridharan, N.S. (ed.) Proceedings of the 11th International Joint Conference on AI, pp. 750–755. Morgan Kaufmann (1989)Google Scholar
  4. 4.
    Chizen, D., Pierson, R.: Global library of women’s medicine (2010)Google Scholar
  5. 5.
    Cordón, O.: Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases, vol. 19. World Scientific Pub. Co. Inc. (2001)Google Scholar
  6. 6.
    Driankov, D., Hellendoorn, H., Reinfrank, M., Ljung, L., Palm, R., Graham, B., Ollero, A.: An Introduction to Fuzzy Control. Springer, Heidelberg (1996)zbMATHGoogle Scholar
  7. 7.
    Hernandez, G., Herrmann, H.J.: Cellular Automata for Elementary Image Enhancement. Graphical Models and Image Processing 58(1), 82–89 (1995)CrossRefGoogle Scholar
  8. 8.
    Gritti, F., Giannotti, E., Nori, J., Bocchi, L.: Active contour segmentation for breast cancer detection using ultrasound images. In: II Congresso Nazionale di Bioingegneria (2010)Google Scholar
  9. 9.
    Huang, Y.L., Chen, D.R.: Watershed segmentation for breast tumor in 2-D sonography. Ultrasound in Medicine & Biology 30(5), 625–632 (2004)CrossRefGoogle Scholar
  10. 10.
    Konouchine, V., Vezhnevets, V.: Interactive image colorization and recoloring based on coupled map lattices. Computer, 231–234 (2006)Google Scholar
  11. 11.
    Mamdani, E., Assilian, S.: An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)zbMATHCrossRefGoogle Scholar
  12. 12.
    Nicolae, M., Moraru, L., Onose, L.: Comparative approach for speckle reduction in medical ultrasound images. Romanian J. Biophys. 20(1), 13–21 (2010)Google Scholar
  13. 13.
    Vezhnevets, V., Konouchine, V.: GrowCut: Interactive multi-label ND image segmentation by cellular automata. In: Proc. of Graphicon, pp. 150–156. Citeseer (2005)Google Scholar
  14. 14.
    Zadeh, L.A.: Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (January 1973)Google Scholar
  15. 15.
    Chi, Z., Yan, H., Pham, T.: Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition. World Scientific (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leonardo Bocchi
    • 1
  • Francesco Rogai
    • 1
  1. 1.Dipartimento di Elettronica e TelecomunicazioniUniversitá degli Studi di FirenzeItaly

Personalised recommendations