Neural Computing and Applications

, Volume 31, Issue 10, pp 6767–6780 | Cite as

Effective segmentations in white blood cell images using \(\epsilon \)-SVR-based detection method

  • Feilong CaoEmail author
  • Yuehua Liu
  • Zhen Huang
  • Jianjun Chu
  • Jianwei Zhao
Original Article


White blood cell (WBC) image detection plays an important role in automatic morphological systems since it can simplify and facilitate WBC segmentation and classification procedures. However, existing WBC detection methods mainly rely on the location of the nucleus, which is found difficult to achieve accurate detection results. This paper proposes a novel WBC detection algorithm through sliding windows with varying sizes to traverse the image for candidates. Three cues are explored to measure the candidates, and a combined cue is used as a single output to distinguish positives from negatives. The \(\epsilon \)-support vector regression is employed to determine the detection window from the candidates. In this paper, two applications of the proposed WBC detection approach are carried out, including an adaptive thresholding algorithm based on WBC detection for nucleus segmentation from images and target detection to lessen the users’ interaction for automatic cytoplasm segmentation.


White blood cell (WBC) image Detection Segmentation Support vector regression GrabCut algorithm 



This work is supported by the National Natural Science Foundation of China under Grant Nos. 61672477 and 61571410

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Sadeghian F, Seman Z, Ramli AR, Kahar BA, Saripan M-I (2009) A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol Proced Online 11(1):196–206CrossRefGoogle Scholar
  2. 2.
    Rezatofighi SH, Soltanian-Zadeh H (2011) Automatic recognition of five types of white blood cells in peripheral blood. Comput Med Image Graph 35(4):333–343CrossRefGoogle Scholar
  3. 3.
    Hiremath P, Bannigidad P, Geeta S (2010) Automated identification and classification of white blood cells (leukocytes) in digital microscopic images. Int J Comput Appl 2:59–63Google Scholar
  4. 4.
    Chinwaraphat S, Sanpanich A, Pintavirooj C, Sangworasil M, Tosranon P (2008) A modified fuzzy clustering for white blood cell segmentation. In: Proceeding of 3rd international symposium biomedical engineering, pp 356–359Google Scholar
  5. 5.
    Jiang K, Liao QM, Dai S-Y (2003) A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering. In: Proceeding of international conference machine Learning cybernetics, vol 5, pp 2820–2825Google Scholar
  6. 6.
    Mohapatra S, Patra D, Kumar S, Satpathy S (2012) Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection. Biomed Eng Lett 2(2):100–110CrossRefGoogle Scholar
  7. 7.
    Putzu L, Caocci G, Di Ruberto C (2014) Leucocyte classification for leukaemia detection using image processing techniques. Artif Intell Med 62(3):179–191CrossRefGoogle Scholar
  8. 8.
    Wu J, Zeng P, Zhou Y Olivier C (2006) A novel color image segmentation method and its application to white blood cell image analysis. In: Proceeding of 8th international conference of signal processing, vol 2Google Scholar
  9. 9.
    Ko BC, Gim JW, Nam J-Y (2011) Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron 42(7):695–705CrossRefGoogle Scholar
  10. 10.
    Pan C, Park DS, Yang Y (2012) Leukocyte image segmentation by visual attention and extreme learning machine. Neural Comput Appl 21(6):1217–1227CrossRefGoogle Scholar
  11. 11.
    Cao F, Cai M, Chu J, Zhao J, Zhou Z (2016) A novel segmentation algorithm for nucleus in white blood cells based on low-rank representation. Neural Comput Appl. CrossRefGoogle Scholar
  12. 12.
    Rezatofighi S, Soltanian-Zadeh H, Sharifia R, Zoroofi R (2009) A new approach to white blood cell nucleus segmentation based on Gram-Schmidt orthogonalization. In: Proceeding of international conference digital image processing, pp 107–111Google Scholar
  13. 13.
    Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369MathSciNetCrossRefGoogle Scholar
  14. 14.
    Felzenszwalb P, Girshick R, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645CrossRefGoogle Scholar
  15. 15.
    Lampert CH, Blaschko M, Hofmann T (2008) Beyond sliding windows: object localization by efficient subwindow search. In: Proceeding of IEEE conference computer vision pattern recognition (CVPR), pp 1–8Google Scholar
  16. 16.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceeding of IEEE conference of computer vision pattern recognition (CVPR), vol 1, pp 886–893Google Scholar
  17. 17.
    Alexe B, Deselaers T, Ferrari V (2012) Measuring the objectness of image windows. IEEE Trans Pattern Anal Mach Intell 34(11):2189–2202CrossRefGoogle Scholar
  18. 18.
    Alexe B, Deselaers T, Ferrari V (2010) What is an object? In: Proceeding of IEEE conference computer vision pattern recognition (CVPR), pp 73–80Google Scholar
  19. 19.
    Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph (TOG) 23:309–314CrossRefGoogle Scholar
  20. 20.
    Handin RI, Lux SE, Stossel TP (2003) Blood: principles and practice of hematology, vol 1. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  21. 21.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698CrossRefGoogle Scholar
  22. 22.
    Vapnik VN (1998) Statistical learning theory, vol 2. Wiley, New YorkzbMATHGoogle Scholar
  23. 23.
    Gonzalez R, Woods R (2008) Digital image processing. Pearson/Prentice Hall, Upper Saddle RiverGoogle Scholar
  24. 24.
    Di Ruberto C, Dempster A, Khan S, Jarra B (2002) Analysis of infected blood cell images using morphological operators. Image Vis Comput 20(2):133–146CrossRefGoogle Scholar
  25. 25.
    Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27CrossRefGoogle Scholar
  26. 26.
    Chinchor N, Sundheim B (1993) Muc-5 evaluation metrics. In: Proceeding of 5th conference of message understanding, pp 69–78Google Scholar
  27. 27.
    Sasaki Y (2007) The truth of the f-measure. Teach Tut Mater VersionGoogle Scholar
  28. 28.
    Tareef A, Song Y, Cai W, Wang Y, Feng DD, Chen M (2016) Automatic nuclei and cytoplasm segmentation of leukocytes with color and texture-based image enhancement. In: 13th IEEE international symposium on biomedical imaging, pp 935–938Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Feilong Cao
    • 1
    Email author
  • Yuehua Liu
    • 1
  • Zhen Huang
    • 1
  • Jianjun Chu
    • 2
  • Jianwei Zhao
    • 1
  1. 1.Department of Applied Mathematics, College of SciencesChina Jiliang UniversityHangzhouPeople’s Republic of China
  2. 2.Jiashan Jasdaq Medical Device Co., Ltd.JiashanPeople’s Republic of China

Personalised recommendations