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Non-parametric and integrated framework for segmenting and counting neuroblastic cells within neuroblastoma tumor images

  • Siamak TafavoghEmail author
  • Karla Felix Navarro
  • Daniel R. Catchpoole
  • Paul J. Kennedy
Original Article

Abstract

Neuroblastoma is a malignant tumor and a cancer in childhood that derives from the neural crest. The number of neuroblastic cells within the tumor provides significant prognostic information for pathologists. An enormous number of neuroblastic cells makes the process of counting tedious and error-prone. We propose a user interaction-independent framework that segments cellular regions, splits the overlapping cells and counts the total number of single neuroblastic cells. Our novel segmentation algorithm regards an image as a feature space constructed by joint spatial-intensity features of color pixels. It clusters the pixels within the feature space using mean-shift and then partitions the image into multiple tiles. We propose a novel color analysis approach to select the tiles with similar intensity to the cellular regions. The selected tiles contain a mixture of single and overlapping cells. We therefore also propose a cell counting method to analyse morphology of the cells and discriminate between overlapping and single cells. Ultimately, we apply watershed to split overlapping cells. The results have been evaluated by a pathologist. Our segmentation algorithm was compared against adaptive thresholding. Our cell counting algorithm was compared with two state of the art algorithms. The overall cell counting accuracy of the system is 87.65 %.

Keywords

Histological image segmentation Neuroblastoma tumor Counting cells Splitting overlapping cells Image partitioning 

References

  1. 1.
    Al-Kofahi Y, Lassoued W, Lee W, Roysam B (2010) Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 57(4):841–852PubMedCrossRefGoogle Scholar
  2. 2.
    Borgefors G (1986) Distance transformations in digital images. IEEE Trans Pattern Anal Mach Intell 34(3):344–371Google Scholar
  3. 3.
    Carletta J (1996) Squibs and discussions assessing agreement on classification tasks: the Kappa statistic. Comput linguist 22(2):249–254Google Scholar
  4. 4.
    Coelho LP, Shariff A, Murphy RF (2009) Nuclear segmentation in microscope cell images: a hand-segmented dataset and comparison of algorithms. In: IEEE international symposium on biomedical imaging, pp 518–21Google Scholar
  5. 5.
    Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619CrossRefGoogle Scholar
  6. 6.
    Dorini LB, Minetto R, Leite NJ (2007) White blood cell segmentation using morphological operators and scale-space analysis. In: IEEE symposium on computer graphics and image processing, Brazil, pp 294–304Google Scholar
  7. 7.
    Fox H (2000) Is H&E morphology coming to an end? J Clin Pathol 53(1):38–40PubMedCrossRefGoogle Scholar
  8. 8.
    Gonzalez RC, Woods RE, Eddins SL (2004) Digital Image Processing using MATLAB. Prentice Hall, Upper Saddle River, New Jersey, pp 13–15Google Scholar
  9. 9.
    Haralick RM, Sternberg SR, Zhuang X (1987) Image analysis using mathematical morphology. IEEE Trans Pattern Anal Mach Intell 4:532–550CrossRefGoogle Scholar
  10. 10.
    Heckbert P (1982) Color image quantization for frame buffer display. ACM, pp 297–303Google Scholar
  11. 11.
    Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70Google Scholar
  12. 12.
    Kim Y, Kim JJ, Won Y, In Y (2003) Segmentation of protein spots in 2D gel electrophoresis images with watersheds using hierarchical threshold. In: Cevat YAS (eds) computer and information sciences-ISCIS, Springer, Hidelberg, pp 389–96Google Scholar
  13. 13.
    Kong H, Gurcan M, Belkacem-Boussaid K (2011) Partitioning histopathological Images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans Med Imaging 30(9):1661–1677PubMedCrossRefGoogle Scholar
  14. 14.
    Lezoray O, Cardot H (2002) Cooperation of color pixel classification schemes and color watershed: a study for microscopic images. IEEE Trans Image Process 11(7):783–789PubMedCrossRefGoogle Scholar
  15. 15.
    Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. In: IEEE computer society conference on computer vision and pattern recognition, San Diego, California, pp 430–6Google Scholar
  16. 16.
    Lovell DP, Omori T (2008) Statistical issues in the use of the comet assay. Mutagenesis 23(3):171–182PubMedCrossRefGoogle Scholar
  17. 17.
    Madhloom H, Kareem S, Ariffin H, Zaidan A, Alanazi H, Zaidan B (2010) An automated white blood cell nucleus localization and segmentation using image arithmetic and automatic threshold. J Appl Sci 10(11):959–966CrossRefGoogle Scholar
  18. 18.
    Mahalanobis PC (1936) On the generalized distance in statistics. In: proceedings of the national institute of science of India, New Delhi, pp 49–55Google Scholar
  19. 19.
    Malpica N, Ortiz de Solorzano C, Vaquero JJ, Santos A, Vallcorba I, Garcia-Sagredo JM, Del Pozo F (1997) Applying watershed algorithms to the segmentation of clustered nuclei. J Cytometry 28(4):289–297CrossRefGoogle Scholar
  20. 20.
    Massey FJ Jr (1951) The Kolmogorov–Smirnov test for goodness of fit. J Amer Statist Assoc 46(253):68–78CrossRefGoogle Scholar
  21. 21.
    Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: IEEE international symposium on biomedical imaging, France, pp 284–7Google Scholar
  22. 22.
    Otsu N (1975) A threshold selection method from gray-level histograms. IEEE Transact Syst 11:285–296Google Scholar
  23. 23.
    Park JR, Eggert A, Caron H (2008) Neuroblastoma: biology, prognosis, and treatment. Hematol Oncol Clin North Am 24(1):65–86CrossRefGoogle Scholar
  24. 24.
    Paschos G (2001) Perceptually uniform color spaces for color texture analysis: an empirical evaluation. IEEE Trans Image Process 10(6):932–937CrossRefGoogle Scholar
  25. 25.
    Qualman SJ, Coffin CM, Newton WA, Hojo H, Triche TJ, Parham DM, Crist WM (1998) Intergroup Rhabdomyosarcoma study: update for pathologists. Pediatr Dev Pathol 1(6):550–561PubMedCrossRefGoogle Scholar
  26. 26.
    Roscie J (2004) Ackerman’s surgical pathology, 10 edn. St. Louis, New York, pp 1070–73Google Scholar
  27. 27.
    Sansone M, Zeni O, Esposito G (2012) Automated segmentation of comet assay images using Gaussian filtering and fuzzy clustering. J Med Biol Eng Comput 50:1–10CrossRefGoogle Scholar
  28. 28.
    Shafarenko L, Petrou M, Kittler J (1997) Automatic watershed segmentation of randomly textured color images. IEEE Trans Image Process 6(11):1530–1544PubMedCrossRefGoogle Scholar
  29. 29.
    Shen DF, Huang MT (2003) A watershed-based image segmentation using JND property. In: IEEE international conference on acoustics, speech and signal processing, pp 377–80Google Scholar
  30. 30.
    Shimada H, Ambors M, Dehner LP, Hata JI, Joshi VV, Roald B (1999) Terminology and morphologic criteria of neuroblastic tumors. Recommendations by the International Neuroblastoma Pathology Committee Cancer 86:349–63Google Scholar
  31. 31.
    Teot LA, Sposto R, Khayat A, Qualman S, Reaman G, Parham D (2007) The problems and promise of central pathology review: development of a standardized procedure for the children’s oncology group. Pediatr Dev Pathol 10(3):199–207PubMedCrossRefGoogle Scholar
  32. 32.
    Wu HS, Berba J, Gil J (2000) Iterative thresholding for segmentation of cells from noisy images. J Microsc 197(3):296–304PubMedCrossRefGoogle Scholar
  33. 33.
    Zhang YJ (1996) A survey on evaluation methods for image segmentation. Pattern Recogn 29(8):1335–1346CrossRefGoogle Scholar
  34. 34.
    Zhou X, Li F, Yan J, Wong STC (2009) A novel cell segmentation method and cell phase identification using Markov model. IEEE Trans Inf Technol Biomed 13(2):152–157PubMedCrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2013

Authors and Affiliations

  • Siamak Tafavogh
    • 1
    Email author
  • Karla Felix Navarro
    • 1
  • Daniel R. Catchpoole
    • 2
  • Paul J. Kennedy
    • 1
  1. 1.Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia
  2. 2.Biospecimens Research and Tumor Bank, Children’s Cancer Research UnitThe Kids Research Institute, The Children’s Hospital at WestmeadWestmeadAustralia

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