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Texture Description for Classification of Fine Needle Aspirates

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Current Trends in Biomedical Engineering and Bioimages Analysis (PCBEE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1033))

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Abstract

In breast cancer diagnosis, fine needle aspiration biopsy is an important diagnostic tool. It is used to estimate cancer malignancy grade that is further required for treatment determination. In this paper we describe a scheme based on pattern recognition and image processing techniques for automatic breast cancer malignancy grading from cytological slides of fine needle aspiration biopsies. To determine a malignancy classification of the slide we propose to extract textural features of nuclei with an application of local binary patterns. Based on texture determination, we present an improved classification system for cancer malignancy grading.

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References

  1. Polish National Cancer Registry, December 2019

    Google Scholar 

  2. Alsaedi, M., Fevens, T., Krzyżak, A., Jeleń, Ł.: Cytological malignancy grading systems for fine needle aspiration biopsies of breast cancer. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 705–709 (2017)

    Google Scholar 

  3. Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PLOS ONE 12(6), 1–14 (2017)

    Article  Google Scholar 

  4. Bloom, H.J.G., Richardson, W.W.: Histological grading and prognosis in breast cancer. Br. J. Cancer 11, 359–377 (1957)

    Article  Google Scholar 

  5. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A. (eds.): Classification and Regression Trees. Chapman & Hall, Boca Raton (1993)

    MATH  Google Scholar 

  6. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience Publishers, New York (2000)

    MATH  Google Scholar 

  7. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  Google Scholar 

  8. Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(4), 463–484 (2012)

    Article  Google Scholar 

  9. Haralick, R.M.: Statistical and structural approaches to texture. IEEE Trans. Syst. Man Cybern. 67(5), 786–804 (1979)

    Google Scholar 

  10. Huang, T.M., Kecman, V., Kopriva, I.: Kernel based algorithms for mining huge data sets: supervised, semi-supervised, and unsupervised learning. Springer (2006)

    Google Scholar 

  11. Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev. Biomed. Eng. 7, 97–114 (2014)

    Article  Google Scholar 

  12. Jeleń, Ł., Krzyżak, A., Fevens, T., Jeleń, M.: Influence of feature set reduction on breast cancer malignancy classification of fine needle aspiration biopsies. Comput. Biol. Med. 79, 80–91 (2016)

    Article  Google Scholar 

  13. Jeleń, Ł., Lipiński, A., Detyna, J., Jeleń, M.: Grading breast cancer malignancy with neural networks. Bio-Algorithms Med. Syst. 2(14), 47–54 (2011)

    Google Scholar 

  14. Kowal, M., Skobel, M., Nowicki, N.: The feature selection problem in computer assisted cytology. Int. J. Appl. Math. Comput. Sci. 28(4), 759–770 (2018)

    Article  MathSciNet  Google Scholar 

  15. Malek, J., Sebri, A., Mabrouk, S., Torki, K., Tourki, R.: Automated breast cancer diagnosis based on GVF-snake segmentation, wavelet features extraction and fuzzy classification. J. Signal Process. Syst. 55(1–3), 49–66 (2009)

    Article  Google Scholar 

  16. Niwas, S.I., Palanisamy, P., Sujathan, K.: Complex wavelet based texture features of cancer cytology images. In: 2010 International Conference on Industrial and Information Systems (ICIIS), pp. 348–353 (2010)

    Google Scholar 

  17. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  18. Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)

    Article  MathSciNet  Google Scholar 

  19. Umbaugh, S.E.: Digital Image Processing and Analysis: Human and Computer Vision Applications with CVIPTools, 2nd edn. CRC Press, New York (2011)

    Google Scholar 

  20. Wang, L., He, D.-C.: Texture classification using texture spectrum. Pattern Recognit. 23(8), 905–910 (1990)

    Article  Google Scholar 

  21. Wolberg, W.H., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci. U.S.A. 87, 9193–9196 (1990)

    Article  Google Scholar 

  22. Zhu, J., Rosset, S., Zou, H., Hastie, T.: Multi-class adaboost. Stat. Interface 2, 349–360 (2006)

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Wrocław University of Science and Technology, wyb. Wyspiańskiego 27, 50-370, Wrocław, Poland

    Łukasz Jeleń

Authors
  1. Łukasz Jeleń
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Correspondence to Łukasz Jeleń .

Editor information

Editors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Gora, Zielona Góra, Poland

    Józef Korbicz

  2. Committee of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland

    Roman Maniewski

  3. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Krzysztof Patan

  4. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Marek Kowal

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Jeleń, Ł. (2020). Texture Description for Classification of Fine Needle Aspirates. In: Korbicz, J., Maniewski, R., Patan, K., Kowal, M. (eds) Current Trends in Biomedical Engineering and Bioimages Analysis. PCBEE 2019. Advances in Intelligent Systems and Computing, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-29885-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-29885-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29884-5

  • Online ISBN: 978-3-030-29885-2

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