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Computational Intelligence Techniques in Image Segmentation for Cytopathology

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Computational Intelligence in Biomedicine and Bioinformatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 151))

Summary

A variety of computational intelligence approaches to nuclei segmentation in the microscope images of fine needle biopsy material is presented in this chapter. The segmentation is one of the most important steps of the automatic medical diagnosis based on the analysis of the microscopic images, and is crucial to making a correct diagnostic decision. Due to complex nature of biological images, standard segmentation methods are not effective enough. In this chapter we present and discuss some modified versions of watershed algorithm, active contours, cellular automata, GrowCut technique, as well as new approaches like fuzzy sets of I and II type, and the sonar-like method.

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References

  1. Ballard, D.: Generalizing the Hough transform to Detect Arbitrary Shapes. Pattern Recogn 13(2), 111–122 (1981)

    Article  MATH  Google Scholar 

  2. Boldrini, J., Costa, M.: An Application of Optimal Control Theory to the Design of Theoretical Schedules of Anticancer Drugs. Int. J. Appl. Math. and Comput. Sci. 9(2), 387–399 (1999)

    MATH  MathSciNet  Google Scholar 

  3. Carlotto, M.: Histogram analysis using a scale space approach. IEEE Trans. Pattern Analysis and Machine Intelligence 9(1), 121–129 (1987)

    Article  Google Scholar 

  4. Chen, C., Luo, J., Parker, K.: Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications. IEEE Trans. Image Processing 7(12), 1673–1683 (1998)

    Article  Google Scholar 

  5. Cheng, H.D., Chen, C.H., Chiu, H.H., Xu, H.: Fuzzy homogeneity approach to multilevel thresholding. IEEE Trans. Image Processing 7(7), 1084–1088 (1998)

    Article  Google Scholar 

  6. Duda, R., Hart, P.: Use of the Hough Transformation to Detect Lines and Curves in Picture. Comm. ACM 15, 11–15 (1972)

    Article  Google Scholar 

  7. Dziekan, Ł., Marciniak, A., Obuchowicz, A.: Segmentation of color cytological images usin type II fuzzy sets. In: Korbicz, J., Patan, K., Kowal, M. (eds.) Fault Diagnosis and Fault Tolerant Control, pp. 263–270. Academic Publishing House EXIT, Warszawa (2007)

    Google Scholar 

  8. Fu, K.S., Mui, J.K.: A survey on image segmentation. Pattern Recogn 13, 3–16 (1981)

    Article  MathSciNet  Google Scholar 

  9. Hrebień, M., Nieczkowski, T., Korbicz, J., Obuchowicz, A.: The Hough transform and the GrowCut method in segmentation of cytological images. In: Proc. Int. Conf. Signals and Electronic Systems ICSES 2006, Łódź, Poland, vol. 1, pp. 367–370 (2006)

    Google Scholar 

  10. Hrebień, M., Korbicz, J., Obuchowicz, A.: Hough transform (1+1) search strategy and watershed algorithm in segmentation of cytological images. In: Proc. 5th Int. Conf. Comp. Recogn. Systems CORES 2007. Adv. in Soft Computing, vol. 45, pp. 550–557. Springer, Heidelberg (2007)

    Google Scholar 

  11. Hrebień, M., Steć, P.: Fine Needle Biopsy Material Segmentation with Hough Transform and Active Contouring Technique. Journal of Medical Inform. & Techn. 10, 25–34 (2007)

    Google Scholar 

  12. Gonzalez, R., Woods, R.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (2002)

    Google Scholar 

  13. Huang, L.K., Wang, M.J.: Image thresholding by minimizing the measure of fuzziness. Pattern Recogn. 28, 41–51 (1995)

    Article  Google Scholar 

  14. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs (1989)

    MATH  Google Scholar 

  15. Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 7, 643–658 (1999)

    Article  Google Scholar 

  16. Kass, M., Witkin, A., Terauzopoulos, D.: Snakes: active contour models. In: Proc. 1st Int. Conf. on Computer Vision, pp. 259–263 (1987)

    Google Scholar 

  17. Kaufmann, A.: Introduction to the Theory of Fuzzy Subsets—Fundamental Theoretical Elements. Academic Press, New York (1975)

    Google Scholar 

  18. Kimmel, M., Lachowicz, M., Świerniak, A. (eds.): Cancer Growth and Progression, Mathematical Problems and Computer Simulations. Int. J. of Appl. Math. and Comput. Sci. 13(3) (2003) (special Issue)

    Google Scholar 

  19. Lee, M., Street, W.: Dynamic learning of shapes for automatic object recognition. In: Proc. 17th Workshop Machine Learning of Spatial Knowledge, pp. 44–49 (2000)

    Google Scholar 

  20. Madisetti, V., Williams, D.: The Digital Signal Processing Handbook. CRC Press, Boca Raton (1997)

    Google Scholar 

  21. Marciniak, A., Monczak, R., Kołodziński, M., Prȩtki, O.A.: Test base for the breast cancer diagnosis using FNB method. In: Proc. Nat. Conf. Artificial Intelligence in Biomedical Engineering SIIB 2004, Kraków, Poland, [4] CD-ROM (2004) (in Polish)

    Google Scholar 

  22. Marciniak, A., Obuchowicz, A., Monczak, R., Kołodziński, M.: Cytomorphometry of Fine Needle Biopsy Material from the Breast Cancer. In: Proc. 4th Int. Conf. Comp. Recogn. Systems CORES 2005. Adv. in Soft Computing, pp. 603–609. Springer, Heidelberg (2005)

    Google Scholar 

  23. Mendel, J.M., John, R.I.: Type-2 Fuzzy Sets Made Simple. IEEE Trans. Fuzzy Systems 10(2), 117–127 (2002)

    Article  Google Scholar 

  24. Mendel, J.M.: An architecture for making judgments using computing with words. Int. J. Appl. Math. Comput. Sci. 12(3), 325–335 (2002)

    MATH  MathSciNet  Google Scholar 

  25. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)

    MATH  Google Scholar 

  26. Nevatia, R.: Image Segmentation. In: Young, T.Y., Fu, K.S. (eds.) Handbook of Pattern Recognition and Image Processing. Academic Press, NY (1986)

    Google Scholar 

  27. Nieczkowski, T., Obuchowicz, A.: Application of cellular automaton for enhancing segmentation results of breast cancer fine needle biopsy microscope images. In: Kłopotek, M., Tchórzewski, J. (eds.) Proceedings of Artificial Intelligence Studies, vol. 3, pp. 71–78. University of Podlasie Press, Siedlce (2006)

    Google Scholar 

  28. Nieczkowski, T., Obuchowicz, A.: ‘Sonar’ - Region of Interest Identification and Segmentation Method for Cytological Breast Cancer Images. In: Proc. 5th Int. Conf. Comp. Recogn. Systems CORES 2007. Adv. in Soft Computing, vol. 45, pp. 566–573. Springer, Heidelberg (2007)

    Google Scholar 

  29. Nieczkowski, T., Obuchowicz, A.: Application of decision trees to filtering and segmentation of breast cancer fine needle biopsy microscope images. Biocybernetics and Biomedical Engineering 27(4), 59–70 (2007)

    Google Scholar 

  30. Obuchowicz, A.: Evolutionary Algorithms for Global Optimization and Dynamic System Diagnosis. Lubuskie Scientific Society Press, Zielona Góra (2003)

    Google Scholar 

  31. Obuchowicz, A., Korbicz, J.: Evolutionary methods in designing diagnostic systems. In: Korbicz, J., Kościelny, J.M., Kowalczuk, Z., Cholewa, W. (eds.) Fault Diagnosis: Models, Artificial Intelligence, Applications, pp. 301–331. Springer, Heidelberg (2004)

    Google Scholar 

  32. Olabarriaga, S.D., Smeulders, A.W.M.: Interaction in the segmentation of medical images: A survey. Medical Image Analysis 5, 127–142 (2001)

    Article  Google Scholar 

  33. Otsu, N.: A threshold selection method from grey-level histograms. IEEE Trans. Systems, Man and Cybernetics 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  34. Pal, N.R., Bezdek, J.C.: Measures of fuzziness: a review and several new classes. In: Yager, R.R., Zadeh, L.A. (eds.) Fuzzy Sets, Neural Networks and Soft Computing, Van Nostrand Reinhold, New York (1994)

    Google Scholar 

  35. Pena-Reyes, C., Sipper, M.: Envolving fuzzy rules for breast cancer diagnosis. In: Proc. Int. Symp. on Nonlinear Theory and Application, vol. 2, pp. 369–372. Polytechniques et Universitaires Romandes Press (1998)

    Google Scholar 

  36. Pratt, W.K.: Digital image processing. Wiley, Chichester (2001)

    Google Scholar 

  37. Russ, J.: The Image Processing Handbook. CRC Press, Boca Raton (1999)

    MATH  Google Scholar 

  38. Setiono, R.: Extracting rules from pruned neural networks for breast cancer diagnosis. Artificial Intelligence in Medicine, 37–51 (1996)

    Google Scholar 

  39. Sethian, J.: Fast marching methods. SIAM Review 41(2) (1999)

    Google Scholar 

  40. Steć, P., Domański, M.: Video Frame Segmentation Using Competitive Contours. In: Proc. 13th European Signal Processing Conference, Antalya, Turkey (2005)

    Google Scholar 

  41. Street, W.: Xcyt: A system for remote cytological diagnosis and prognosis of breast cancer. In: Jain, L. (ed.) Soft Computing Techniques in Breast Cancer Prognosis and Diagnosis, pp. 297–322. World Scientific Publishing, Singapore (2000)

    Google Scholar 

  42. Su, M., Chou, C.: A modified version of the K-means algorithm with a distance based on cluster symmetry. IEEE Trans. Pattern Analysis and Machine Intelligence 23(6), 674–680 (2001)

    Article  Google Scholar 

  43. Świerniak, A., Ledzewicz, U., Schättler, H.: Optimal Control for a Class of Compartmental Models in Cancer Chemotherapy. Int. J. of Appl. Math. and Comput. Sci. 13(3), 357–368 (2003)

    MATH  Google Scholar 

  44. Tadeusiewicz, R.: Vision Systems of Industrial Robots. WNT, Warszawa (in Polish) (1992)

    Google Scholar 

  45. Tizhoosh, H.R.: Image thresholding using type II fuzzy sets. Pattern Recognition 38, 2363–2372 (2005)

    Article  Google Scholar 

  46. Vezhnevets, V., Konouchine, V.: “GrowCut” – Interactive Multi-Label N-D Image Segmentation by Cellular Automata. In: Proc. 15th Int. Conf. on Comp. Graphics and Appl. GraphiCon 2005, Novosibirsk, Russia, pp. 150–156 (2005)

    Google Scholar 

  47. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)

    Article  Google Scholar 

  48. Wolberg, W., Street, W., Mangasarian, O.: Breast cytology diagnosis via digital image analysis. Analytical and Quantitative Cytology and Histology 15(6), 396–404 (1993)

    Google Scholar 

  49. Zhou, P., Pycock, D.: Robust statistical models for cell image interpretation. Image and Vision Computing 15(4), 307–316 (1997)

    Article  Google Scholar 

  50. Zouagui, T., Benoit-Cattin, H., Odet, C.: Image segmentation functional model. Pattern Recognition 37(9), 1785–1795 (2002)

    Article  Google Scholar 

  51. Żorski, W.: Image Segmentation Methods Based on the Hough Transform. Studio GiZ, Warszawa (in Polish) (2000)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246, Zielona Góra, Poland

    Andrzej Obuchowicz, Maciej Hrebień, Tomasz Nieczkowski & Andrzej Marciniak

Authors
  1. Andrzej Obuchowicz
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  2. Maciej Hrebień
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  3. Tomasz Nieczkowski
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  4. Andrzej Marciniak
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Editor information

Editors and Affiliations

  1. Department of Biology, Emory University, 1510 Clifton Rd.NE, 30322, Atlanta, Georgia, USA

    Tomasz G. Smolinski

  2. Department of Computer Science, University of Arkansas at Little Rock, 2801 S.University Ave., 72204, Little Rock, Arkansas, USA

    Mariofanna G. Milanova

  3. Department of Quantitative Methods and Information Systems College of Business and Administration, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

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Obuchowicz, A., Hrebień, M., Nieczkowski, T., Marciniak, A. (2008). Computational Intelligence Techniques in Image Segmentation for Cytopathology. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Computational Intelligence in Biomedicine and Bioinformatics. Studies in Computational Intelligence, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70778-3_7

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  • DOI: https://doi.org/10.1007/978-3-540-70778-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70776-9

  • Online ISBN: 978-3-540-70778-3

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