Medical & Biological Engineering & Computing

, Volume 57, Issue 6, pp 1265–1283 | Cite as

Color clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood

  • Santiago AlférezEmail author
  • Anna Merino
  • Andrea Acevedo
  • Laura Puigví
  • José Rodellar
Original Article


Current computerized image systems are able to recognize normal blood cells in peripheral blood, but fail with abnormal cells like the classes of lymphocytes associated to lymphomas. The main challenge lies in the subtle differences in morphologic characteristics among these classes, which requires a refined segmentation. A new efficient segmentation framework has been developed, which uses the image color information through fuzzy clustering of different color components and the application of the watershed transformation with markers. The final result is the separation of three regions of interest: nucleus, entire cell, and peripheral zone around the cell. Segmentation of this zone is crucial to extract a new feature to identify cells with hair-like projections. The segmentation is validated, using a database of 4758 cell images with normal, reactive lymphocytes and five types of malignant lymphoid cells from blood smears of 105 patients, in two ways: (1) the efficiency in the accurate separation of the regions of interest, which is 92.24%, and (2) the accuracy of a classification system implemented over the segmented cells, which is 91.54%. In conclusion, the proposed segmentation framework is suitable to distinguish among abnormal blood cells with subtile color and spatial similarities.

Graphical Abstract

The segmentation framework uses the image color information through fuzzy clustering of different color components and the application of the watershed transformation with markers (Top). The final result is the separation of three regions of interest: nucleus, entire cell, and peripheral zone around the cell. The procedure is also validated by the implementation of a system to automatically classify different types of abnormal blood cells (Bottom)


Biomedical image processing Cell morphology Clustering methods Image segmentation Optical microscopy Clinical pathology 



This work was partially supported by the Spanish Ministry of Economy and Competitiveness under Grant DPI2015-64493-R (MINECO/FEDER) and by the Generalitat de Catalunya under Grant SGR-859-2014.


  1. 1.
    Alférez S (2015) Methodology for automatic classification of atypical lymphoid cells from peripheral blood cell images. PhD thesis, Universitat Politècnica de CatalunyaGoogle Scholar
  2. 2.
    Alférez S, Merino A, Mujica LE, Ruiz M, Bigorra L, Rodellar J (2014) Automatic classification of atypical lymphoid B cells using digital blood image processing. Int J Lab Hematol 36(4):472–80. Google Scholar
  3. 3.
    Alférez S, Merino A, Bigorra L, Mujica L, Ruiz M, Rodellar J (2015) Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis. Am J Clin Pathol 143:168–176. Google Scholar
  4. 4.
    Alférez S, Merino A, Bigorra L, Rodellar J (2016) Characterization and automatic screening of reactive and abnormal neoplastic b lymphoid cells from peripheral blood. Int J Lab Hematol 38(2):209–219. Google Scholar
  5. 5.
    Angulo J (2003) Morphologie mathématique et indexation d’images couleur: application à la microscopie en biomédecine. PhD thesis, Mines ParisTechGoogle Scholar
  6. 6.
    Angulo J, Klossa J, Flandrin G (2006) Ontology-based lymphocyte population description using mathematical morphology on colour blood images. Cell Mol Biol 52(6):2–15. Google Scholar
  7. 7.
    Arslan S, Ozyurek E, Gunduz-Demir C (2014) A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images. Cytom Part A 85(6):480–490. Google Scholar
  8. 8.
    Balafar MA, Ramli AR, Saripan MI, Mashohor S (2010) Review of brain MRI image segmentation methods. Artif Intell Rev 33(3):261–74Google Scholar
  9. 9.
    Beucher S (1992) The watershed transformation applied to image segmentation. Scanning Microsc Suppl 6:299–314. Google Scholar
  10. 10.
    Bezdek JC (1981) Objective function clustering. In: Pattern recognition with fuzzy objective function algorithms. Advanced applications in pattern recognition. Springer, Boston, DOI, (to appear in print)
  11. 11.
    Brown G, Pocock A, Zhao MJ, Luján M (2012) Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J Mach Learn Res 13(1):27–66Google Scholar
  12. 12.
    Cellavision (2016) Digital cell morphology. Retrieved from:, (Accessed 2017)
  13. 13.
    Centre of mathematical morphology MINES ParisTech (2014) Image segmentation and mathematical morphology. Retrieved from:, (Accesed 2017)
  14. 14.
    Chen S, Zhang D (2004) Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern Part B 34(4):1907–1916. Google Scholar
  15. 15.
    Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15. Google Scholar
  16. 16.
    Comaniciu D, Meer P, Foran DJ (1999) Image-guided decision support system for pathology. Mach Vis Appl 11(4):213–224. Google Scholar
  17. 17.
    Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeGoogle Scholar
  18. 18.
    Dembele D, Kastner P (2003) Fuzzy C-means method for clustering microarray data. Bioinformatics 19 (8):973–80Google Scholar
  19. 19.
    Dorini LB, Minetto R, Leite N (2012) Semi-automatic white blood cell segmentation based on multiscale analysis. IEEE Trans Inf Technol Biomed 17(1):250–256. Google Scholar
  20. 20.
    Ghosh M, Das D, Chakraborty C, Ray AK (2010) Automated leukocyte recognition using fuzzy divergence. Micron 41(7):840–846. Google Scholar
  21. 21.
    Gönen M, Alpaydin E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12(Jul):2211–2268Google Scholar
  22. 22.
    Gonzalez J, Olmos I, Altamirano L, Morales BA, Reta C, Galindo MC, Alonso JE, Lobato R (2011) Leukemia identification from bone marrow cells images using a machine vision and data mining strategy. Intell Data Anal 15:443–462. Google Scholar
  23. 23.
    Gutiérrez G, Merino A, Domingo A, Jou JM, Reverter JC (2008) Eqas for peripheral blood morphology in spain: a 6-year experience. Int J Lab Hematol 30(6):460–6. Google Scholar
  24. 24.
    Houwen B (2001) The differential cell count. Lab Hematol 7(2):89–100Google Scholar
  25. 25.
    Madhloom HT, Kareem SA, Ariffin H (2012) A robust feature extraction and selection method for the recognition of lymphocytes versus acute lymphoblastic leukemia. 2012 Int Conf Adv Comput Sci Appl Technol :330–335.
  26. 26.
    Madhloom HT, Kareem SA, Ariffin H, HA Zaidan AA, Zaidan B (2010) An automated white blood cell nucleus localization and segmentation using image arithmetic and automatic threshold. J Appl Sci 10 (11):959–966. Google Scholar
  27. 27.
    Markiewicz T, Osowski S, Mariańska B (2007) White blood cell automatic counting system based on support vector machine. In: Beliczynski B, Dzielinski A, Iwanowski M, Ribeiro B (eds) Adaptive and natural computing algorithms. ICANNGA 2007. Lecture notes in computer science, vol 4432. Springer, Berlin, DOI
  28. 28.
    Medica (2016) Easycell assistant. Retrieved from:, products/hematology-imaging-analyzers (Accessed 2016)
  29. 29.
    Merino A, Puigví L, Boldú L, Alférez S, Rodellar J (2018) Optimizing morphology through blood cell image analysis. Int J Lab Hematol 40(Suppl. 1):54–61. Google Scholar
  30. 30.
    Meyer F (1994) Topographic distance and watershed lines. Signal Process 38(1):113–125Google Scholar
  31. 31.
    Mohapatra S, Patra D (2010) Automated leukemia detection using hausdorff dimension in blood microscopic images. In: 2010 Int Conf, IEEE, Ieee, Emerg Trends Robot Commun Technol (INTERACT), pp 64–68.
  32. 32.
    Mohapatra S, Samanta SS, Patra D, Satpathi S (2011) Fuzzy based blood image segmentation for automated leukemia detection. 2011 Int Conf Devices Commun :1–5.
  33. 33.
    Muller K, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to kernel-based learning algorithms. Neural Networks IEEE Trans 12(2):181–201Google Scholar
  34. 34.
    Münzenmayer C, Schlarb T, Steckhan D, Haßlmeyer E, Bergen T, Aschenbrenner S, Wittenberg T, Weigand C, Zerfaß T (2011) Hemacam - a computer assisted microscopy system for hematology. Springer, pp 233–242Google Scholar
  35. 35.
    Nikolaou N, Papamarkos N (2009) Color reduction for complex document images. Int J Imaging Syst Technol 19(1):14–2Google Scholar
  36. 36.
    Pal NR, Bezdek JC (1995) On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst 3 (3):370–9Google Scholar
  37. 37.
    Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–38. Google Scholar
  38. 38.
    Plissiti ME, Nikou C, Charchanti A (2011) Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering. IEEE Trans Inf Technol Biomed 15(2):233–41. Google Scholar
  39. 39.
    Putzu L, Caocci Gi, Di Ruberto C (2014) Leucocyte classification for leukaemia detection using image processing techniques. Artif Intell Med 62(3):179–191Google Scholar
  40. 40.
    Ramoser H, Laurain V, Bischof H, Ecker R (2006) Leukocyte segmentation and classification in blood-smear images. In: Eng Med Biol Soc 2005 IEEE-EMBS 2005 27th Annu Int Conf, IEEE, vol 4, pp 3371–3374.
  41. 41.
    Rezatofighi SH, Soltanian-Zadeh H (2011) Automatic recognition of five types of white blood cells in peripheral blood. Comput Med Imaging Graph 35(4):333–343Google Scholar
  42. 42.
    Roerdink J, Meijster A (2000) The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41:1–40Google Scholar
  43. 43.
    Ross TJ (2009) Fuzzy logic with engineering applications. Wiley, New JerseyGoogle Scholar
  44. 44.
    Sabino DMU, Dafontouracosta L, Gilrizzatti E, Antoniozago M, da Fontoura Costa L, Gil Rizzatti E, Antonio Zago M (2004) A texture approach to leukocyte recognition. Real-Time Imaging 10(4):205–216. Google Scholar
  45. 45.
    Sadeghian F, Seman Z, Ramli AR, Abdul Kahar BH, Saripan MI (2009) A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol Proced Online 11(1):196–206. Google Scholar
  46. 46.
    Scotti F (2005) Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. In: CIMSA. 2005 IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl. 2005., IEEE, July., pp 96–101
  47. 47.
    Scotti F (2006) Robust segmentation and measurements techniques of white cells in blood microscope images. 2006 IEEE Instrum Meas Technol Conf Proc (April):43–48,
  48. 48.
    Sinha N, Ramakrishnan A (2003) Automation of differential blood count. TENCON 2003 Conf (i)Google Scholar
  49. 49.
    Snoek J, Larochelle H, Adams RP (2012) Practical bayesian optimization of machine learning algorithms. arXiv:1206.2944
  50. 50.
    Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, Advani R, Ghielmini M, Salles GA, Zelenetz AD, Jaffe ES (2016) The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood 127(20):2375–2390. Google Scholar
  51. 51.
    Tuzel O, Yang L, Meer P, Foran DJ (2007) Classification of hematologic malignancies using texton signatures. Pattern Anal Appl PAA 10(4):277–290. Google Scholar
  52. 52.
    Yang L, Meer P, Foran DJ (2005) Unsupervised segmentation based on robust estimation and color active contour models. IEEE Trans Inf Technol Biomed 9(3):475–86. Google Scholar
  53. 53.
    Yang L, Tuzel O, Chen W, Meer P, Salaru G, Goodell LA, Foran DJ (2009) Pathminer: a web-based tool for computer-assisted diagnostics in pathology. IEEE Trans Inf Technol Biomed 13(3):291–9. Google Scholar
  54. 54.
    Zhang D, Chen S (2002) Fuzzy clustering using kernel method. In: 2002 Int. Conf. Control Autom. 2002. ICCAGoogle Scholar
  55. 55.
    Zhang DQ, Chen SC (2004) A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med 32(1):37–50. Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Mathematics, EEBETechnical University of CataloniaBarcelonaSpain
  2. 2.Biomedical Diagnostic Center in the Hospital ClinicBarcelonaSpain

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