SpringerLink
Log in

Fitness Functions Evaluation for Segmentation of Lymphoma Histological Images Using Genetic Algorithm

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

For disease monitoring, grade definition and treatments orientation, specialists analyze tissue samples to identify structures of different types of cancer. However, manual analysis is a complex task due to its subjectivity. To help specialists in the identification of regions of interest, segmentation methods are used on histological images obtained by the digitization of tissue samples. Besides, features extracted from these specific regions allow for more objective diagnoses by using classification techniques. In this paper, fitness functions are analyzed for unsupervised segmentation and classification of chronic lymphocytic leukemia and follicular lymphoma images by the identification of their neoplastic cellular nuclei through the genetic algorithm. Qualitative and quantitative analyses allowed the definition of the Rényi entropy as the most adequate for this application. Images classification has reached results of 98.14% through accuracy metric by using this fitness function.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Orlov, N.V., Chen, W.W., Eckley, D.M., Macura, T.J., Shamir, L., Jaffe, E.S., Goldberg, I.G.: Automatic classification of lymphoma images with transform-based global features. IEEE Trans. Inf. Technol. Biomed. 14(4), 1003–1013 (2010)

    Article  Google Scholar 

  2. Lowry, L., Linch, D.: Non-Hodgkin’s lymphoma (2013)

    Google Scholar 

  3. Belkacem-Boussaid, K., Samsi, S., Lozanski, G., Gurcan, M.N.: Automatic detection of follicular regions in H&E images using iterative shape index. Comput. Med. Imaging Graph. 35(7), 592–602 (2011)

    Article  Google Scholar 

  4. Canellos, G.P., Lister, T.A., Young, B.: The Lymphomas, 2nd edn. Saunders Elsevier, Philadelphia (2006)

    Google Scholar 

  5. Mohammed, E.A., Far, B.H., Naugler, C., Mohamed, M.M.A.: Chronic lymphocytic leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding. In: 26th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–5. IEEE (2013)

    Google Scholar 

  6. Mohammed, E.A., Far, B.H., Naugler, C., Mohamed, M.M.A.: Application of support vector machine and k-means clustering algorithms for robust chronic lymphocytic leukemia color cell segmentation. In: 15th International Conference on e-Health Networking, Applications and Services. IEEE (2013)

    Google Scholar 

  7. Sertel, O., Kong, J., Lozanski, G., Catalyurek, U., Saltz, J.H., Gurcan, M.N.: Computerized microscopic image analysis of follicular lymphoma. In: Medical Imaging, vol. 6915. International Society for Optics and Photonics (2008)

    Google Scholar 

  8. Kong, H., Belkacem-Boussaid, K., Gurcan, M.: Cell nuclei segmentation for histopathological image analysis. In: SPIE Medical Imaging, p. 79622R. International Society for Optics and Photonics (2011)

    Google Scholar 

  9. Belkacem-Boussaid, K., Prescott, J., Lozanski, G., Gurcan, M.N.: Segmentation of follicular regions on H&E slides using a matching filter and active contour model. In: SPIE Medical Imaging. International Society for Optics and Photonics (2010)

    Google Scholar 

  10. Oztan, B., Kong, H., Gurcan, M.N., Yener, B.: Follicular lymphoma grading using cell-graphs and multi-scale feature analysis. In: SPIE Medical Imaging, vol. 8315 (2012)

    Google Scholar 

  11. Oger, M., Belhomme, P., Gurcan, M.N.: A general framework for the segmentation of follicular lymphoma virtual slides. Comput. Med. Imaging Graph. 36(6), 442–451 (2012)

    Article  Google Scholar 

  12. Dimitropoulos, K., Barmpoutis, P., Koletsa, T., Kostopoulos, I., Grammalidis, N.: Automated detection and classification of nuclei in pax5 and h&e-stained tissue sections of follicular lymphoma. Signal Image Video Process. 1–9 (2016)

    Google Scholar 

  13. Dimitropoulos, K., Barmpoutis, P., Koletsa, T., Kostopoulos, I., Grammalidis, N.: Classification of nuclei in follicular lyphoma tissue sections using different stains and bayesian networks. In: Kyriacou, E., Christofides, S., Pattichis, C.S. (eds.) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IP, vol. 57, pp. 234–238. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32703-7_47

    Chapter  Google Scholar 

  14. Dimitropoulos, K., Michail, E., Koletsa, T., Kostopoulos, I., Grammalidis, N.: Using adaptive neuro-fuzzy inference systems for the detection of centroblasts in microscopic images of follicular lymphoma. Signal Image Video Process. 8(1), 33–40 (2014)

    Article  Google Scholar 

  15. Luo, Y., Celenk, M., Bejai, P.: Discrimination of malignant lymphomas and leukemia using radon transform based-higher order spectra. In: Medical Imaging, pp. 61445K-1–61445K-10. International Society for Optics and Photonics (2006)

    Google Scholar 

  16. Sertel, O., Kong, J., Lozanski, G., Catalyurek, U., Saltz, J.H., Gurcan, M.N.: Histopathological image analysis using model-based intermediate representations and color texture: follicular lymphoma grading. J. Signal Process. Syst. 55(1–3), 169–183 (2009)

    Article  Google Scholar 

  17. Sertel, O., Kong, J., Lozanski, G., Shana’ah, A., Catalyurek, U., Saltz, J., Gurcan, M.: Texture classification using nonlinear color quantization: application to histopathological image analysis. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 597–600. IEEE (2008)

    Google Scholar 

  18. McCann, M.T., Ozolek, J.A., Castro, C.A., Parvin, B., Kovacevic, J.: Automated histology analysis: opportunities for signal processing. Signal Process. Mag. 32(1), 78–87 (2015)

    Article  Google Scholar 

  19. Oswal, V., Belle, A., Diegelmann, R., Najarian, K.: An entropy-based automated cell nuclei segmentation and quantification: application in analysis of wound healing process. Comput. Math. Meth. Med. 2013, 1–10 (2013)

    Article  MathSciNet  Google Scholar 

  20. Tang, L., Tian, L., Steward, B.L.: Color image segmentation with genetic algorithm for in-field weed sensing. Trans. ASAE 43(4), 1019 (2000)

    Article  Google Scholar 

  21. Maulik, U.: Medical image segmentation using genetic algorithms. IEEE Trans. Inf. Technol. Biomed. 13(2), 166–173 (2009)

    Article  Google Scholar 

  22. Shamir, L., Orlov, N., Eckley, D.M., Macura, T.J., Goldberg, I.G.: IICBU 2008: a proposed benchmark suite for biological image analysis. Med. Biol. Eng. Comput. 46(9), 943–947 (2008)

    Article  Google Scholar 

  23. Wang, P., Hu, X., Li, Y., Liu, Q., Zhu, X.: Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Sig. Process. 122, 1–13 (2016)

    Article  Google Scholar 

  24. Zorman, M., Kokol, P., Lenic, M., Rosa, J.L.S., Sigut, J.F., Alayon, S.: Symbol-based machine learning approach for supervised segmentation of follicular lymphoma images. In: 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS), pp. 115–120. IEEE (2007)

    Google Scholar 

  25. Gonzalez, R.C., Woods, R.E.: Processamento de Imagens Digitais. Edgard Blucher, São Paulo (2000)

    Google Scholar 

  26. Yin, S., Zhao, X., Wang, W., Gong, M.: Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization. Pattern Recogn. 47(9), 2894–2907 (2014)

    Article  Google Scholar 

  27. Abo-Eleneen, Z., Abdel-Azim, G.: A novel algorithm for image thresholding using non-parametric fisher information. In: International Electronic Conference on Entropy and Its Applications, vol. 1, p. 15. Multidisciplinary Digital Publishing Institute (2014)

    Google Scholar 

  28. Sarkar, S., Das, S., Chaudhuri, S.S.: Hyper-spectral image segmentation using rényi entropy based multi-level thresholding aided with differential evolution. Expert Syst. Appl. 50, 120–129 (2016)

    Article  Google Scholar 

  29. Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3–55 (2001)

    Article  MathSciNet  Google Scholar 

  30. Zhang, Y., Wu, L.: Optimal multi-level thresholding based on maximum tsallis entropy via an artificial bee colony approach. Entropy 13(4), 841–859 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  31. Hammouche, K., Diaf, M., Siarry, P.: A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput. Vis. Image Underst. 109(2), 163–175 (2008)

    Article  Google Scholar 

  32. Ng, H.: Automatic thresholding for defect detection. Pattern Recogn. Lett. 27(14), 1644–1649 (2006)

    Article  Google Scholar 

  33. Dong, F., Irshad, H., Oh, E., Lerwill, M.F., Brachtel, E.F., Jones, N.C., Knoblauch, N.W., Montaser-Kouhsari, L., Johnson, N.B., Rao, L.K., et al.: Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PLoS ONE 9(12), e114885 (2014)

    Article  Google Scholar 

  34. Abbas, Q.: Segmentation of differential structures on computed tomography images for diagnosis lung-related diseases. Biomed. Signal Process. Control 33, 325–334 (2017)

    Article  Google Scholar 

  35. Semler, L., Dettori, L., Furst, J.: Wavelet-based texture classification of tissues in computed tomography. In: IEEE Symposium on Computer-Based Medical Systems, pp. 265–270. IEEE (2005)

    Google Scholar 

  36. Chang, V., Saavedra, J.M., Castañeda, V., Sarabia, L., Hitschfeld, N., Härtel, S.: Gold-standard and improved framework for sperm head segmentation. Comput. Methods Programs Biomed. 117(2), 225–237 (2014)

    Article  Google Scholar 

  37. Unal, I.: Defining an optimal cut-point value in ROC analysis: an alternative approach. Comput. Math. Meth. Med. 2017, 1–14 (2017)

    Article  Google Scholar 

  38. Liu, X.: Classification accuracy and cut point selection. Stat. Med. 31(23), 2676–2686 (2012)

    Article  MathSciNet  Google Scholar 

  39. Vahadane, A., Sethi, A.: Towards generalized nuclear segmentation in histological images. In: International Conference Bioinformatics and Bioengineering, pp. 1–4. IEEE (2013)

    Google Scholar 

  40. Phoulady, H.A., Goldgof, D.B., Hall, L.O., Mouton, P.R.: Nucleus segmentation in histology images with hierarchical multilevel thresholding. In: SPIE Medical Imaging, p. 979111. International Society for Optics and Photonics (2016)

    Google Scholar 

Download references

Acknowledgments

T.A.A.T. and M.Z.N. thank to CAPES (1575210) and FAPEMIG (TEC - APQ-02885-15 project) for financial support.

Author information

Authors and Affiliations

  1. Center of Mathematics, Computing and Cognition, Federal University of ABC, Santo André, Brazil

    Thaína A. A. Tosta & Marcelo Zanchetta do Nascimento

  2. Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia, Uberlândia, Brazil

    Paulo Rogério de Faria

  3. Department of Computer Science and Statistics, São Paulo State University, São José do Rio Preto, Brazil

    Leandro Alves Neves

  4. Faculty of Computer Science, Federal University of Uberlândia, Uberlândia, Brazil

    Marcelo Zanchetta do Nascimento

Authors
  1. Thaína A. A. Tosta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo Rogério de Faria
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leandro Alves Neves
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcelo Zanchetta do Nascimento
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thaína A. A. Tosta .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tosta, T.A.A., de Faria, P.R., Neves, L.A., do Nascimento, M.Z. (2018). Fitness Functions Evaluation for Segmentation of Lymphoma Histological Images Using Genetic Algorithm. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77538-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation