Automatic Nuclei Detection on Cytological Images Using the Firefly Optimization Algorithm

  • Paweł Filipczuk
  • Weronika Wojtak
  • Andrzej Obuchowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7339)


The firefly algorithm is a powerful optimization method inspired by the flashing behavior of fireflies. In our work on computer aided breast cancer diagnosis we met a problem of automatic marking of nuclei. Our system is based on analysis of microscopic images of fine needle biopsy material. The task of the system is to identify benign and malignant lesions (optionally it can also distinguish fibroadenoma). For this purpose it extracts nuclei from cytological images in segmentation phase, then it determines their morphometric features and finally classifies the case. Some segmentation methods require a preliminary selection of objects on the image. We have adapted the firefly algorithm to this task. We have also proposed an initialization procedure. The method was experimentally shown to be satisfactorily effective. The approach was tested with real case medical data collected from patients of the Regional Hospital in Zielona Góra.


firefly algorithm image analysis nuclei detection computer-aided diagnosis breast cancer 


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  1. 1.
    Filipczuk, P., Kowal, M., Marciniak, A.: Feature selection for breast cancer malignancy classification problem. J. Medical Informatics & Technologies 15, 193–199 (2010)Google Scholar
  2. 2.
    Filipczuk, P., Kowal, M., Obuchowicz, A.: Automatic Breast Cancer Diagnosis Based on K-Means Clustering and Adaptive Thresholding Hybrid Segmentation. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 3. AISC, vol. 102, pp. 295–302. Springer, Heidelberg (2011) ISBN: 978-3-642-23153-7CrossRefGoogle Scholar
  3. 3.
    Filipczuk, P., Kowal, M., Obuchowicz, A.: Fuzzy Clustering and Adaptive Thresholding Based Segmentation Method for Breast Cancer Diagnosis. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds.) Computer Recognition Systems 4. AISC, vol. 95, pp. 613–622. Springer, Heidelberg (2011) ISBN: 978-3-642-20319-0CrossRefGoogle Scholar
  4. 4.
    Gil, J., Wu, H., Wang, B.J.: Image analysis and morphometry in the diagnosis. J. Microsc. Res. Tech. 59, 109–118 (2002)CrossRefGoogle Scholar
  5. 5.
    Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: A review. IEEE Reviews in Biomedical Engineering 2, 147–171 (2009)PubMedCrossRefGoogle Scholar
  6. 6.
    Hrebień, M., Steć, P., Obuchowicz, A., Nieczkowski, T.: Segmentation of breast cancer fine needle biopsy cytological images. Int. J. Appl. Math. and Comp. Sci. 18(2), 159–170 (2010)CrossRefGoogle Scholar
  7. 7.
    Jeleń, L., Fevens, T., Krzyżak, A.: Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies. Int. J. Appl. Math. and Comp. Sci. 18(1), 75–83 (2010)Google Scholar
  8. 8.
    Kowal, M., Filipczuk, P., Korbicz, J.: Hybrid cytological image segmentation method based on competitive neural network and adaptive thresholding. Pomiary, Automatyka, Kontrola 57(11), 1448–1451 (2011)Google Scholar
  9. 9.
    Kowal, M., Korbicz, J.: Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds.) Advances in Machine Learning I. SCI, vol. 262, pp. 405–417. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Marciniak, A., Obuchowicz, A., Monczak, A., Kołodziński, M.: Cytomorphometry of fine needle biopsy material from the breast cancer. In: Proc. 4th Int. Conf. on Computer Recognition Systems, CORES 2005, pp. 603–609 (2005)Google Scholar
  11. 11.
    Śmietanski, J., Tadeusiewicz, R., Łuczyńska, E.: Texture analysis in perfusion images of prostate cancer - a case study. Int. J. Appl. Math. and Comp. Sci. 20(1), 149–156 (2010)CrossRefGoogle Scholar
  12. 12.
    Underwood, J.C.E.: Introduction to biopsy interpretation and surgical pathology. Springer, London (1987)CrossRefGoogle Scholar
  13. 13.
    Automata Vladimir Vezhnevets. Growcut - interactive multi-label n-d image segmentation by cellular (2005)Google Scholar
  14. 14.
    Wolberg, W.H., Street, W.N., Mangasarian, O.L.: Breast cytology diagnosis via digital image analysis. Analytical and Quantitative Cytology and Histology 15, 396–404 (1993)PubMedGoogle Scholar
  15. 15.
    Li, H., Yang, X., Zhou, X.: Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy. IEEE Trans. on Circuits and Systems I: Regular Papers 53(11), 2405–2414 (2006)CrossRefGoogle Scholar
  16. 16.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paweł Filipczuk
    • 1
  • Weronika Wojtak
    • 2
  • Andrzej Obuchowicz
    • 1
  1. 1.Institute of Control & Computation EngineeringZielona GóraPoland
  2. 2.Faculty of Electrical Engineering, Computer Science and TelecommunicationsZielona GóraPoland

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