Multimedia Tools and Applications

, Volume 77, Issue 9, pp 10485–10500 | Cite as

Active contour model-based segmentation algorithm for medical robots recognition

  • Yujie Li
  • Yun Li
  • Hyoungseop Kim
  • Seiichi Serikawa


In this paper, an identifying and classifying algorithm is proposed to solve the problem of recognizing objects accurately and effectively. First, via image preprocessing, initial images are obtained via denoising, smoothness, and image erosion. Then, we use granularity analysis and morphology methods to recognize the objects. For small objects identification and to analyze the objects, we calculate four characteristics of each cell: area, roundness, rectangle factor, and elongation. Finally, we segment the cells using the modified active contour method. In addition, we apply chromatic features to recognize the blood cancer cells. The algorithm is tested on multiple collected clinical cases of blood cell images. The results prove that the algorithm is valid and efficient when recognizing blood cancer cells and has relatively high accuracy rates for identification and classification. The experimental results also certificate the effectiveness of the proposed method for extracting precise, continuous edges with limited human intervention, especially for images with neighboring or overlapping blood cells. In addition, the results of the experiments show that this algorithm can accelerate the detection velocity.


Active contour model Granularity detection Cancer cell recognition 



This work was supported by JSPS KAKENHI (No.15F15077), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Research Fund of Chinese Academy of Sciences (No.MGE2015KG02), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1608), and Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1315; 1510).


  1. 1.
    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277CrossRefMATHGoogle Scholar
  2. 2.
    Chen XQ (2016) Fractal dimension estimation for developing pathological brain detection system based on Minkowski-Bouligand method. IEEE Access 4:5937–5947CrossRefGoogle Scholar
  3. 3.
    Chen Y, Zhang Y, Lu H, Chen X, Li J, Wang S (2016) Wavelet energy entropy and linear regression classifier for detecting abnormal breasts. Multimedia Tools Appl :1–20.  10.1007/s11042-016-4161-0
  4. 4.
    Cremers D, Rousson M (2007) A review of statistical approaches to level set segmentation: integrating color, texture, motion, and shape. Int J Comput Vis 72(2):195–215Google Scholar
  5. 5.
    Funt B, Bemard K, Martin L (1998) Is machine color constancy good enough. In: Proceeding of 5th European conference on computer vision. Year of Publication: 1998 ISBN: 3-540-64569-1, pp 445–459Google Scholar
  6. 6.
    Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(1):321–331CrossRefMATHGoogle Scholar
  7. 7.
    Kass M, Witkin A, Terzopoulos D (1998) Snakes: active contour models. Int J Comput Vis 1(4):321–338CrossRefGoogle Scholar
  8. 8.
    Kühne G, Weickert J, Beier M, Effelsberg W (2002) Fast implicit active contour models. In: Van Gool L (eds) Pattern recognition. DAGM 2002. Lecture notes in computer science, vol 2449. Springer, Berlin, HeidelbergGoogle Scholar
  9. 9.
    Li C, Kao C, Gore J, Ding Z (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10):1940–1949MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Li Y, Lu H, Li J, Li X, Serikawa S (2016) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 54:68–77CrossRefGoogle Scholar
  11. 11.
    Li Y, Lu H, Zhang L, Serikawa S (2011) An improved detection algorithm based on morphology methods for blood cancer cells detection. J Comput Inf Syst 7(13):4724–4731Google Scholar
  12. 12.
    Li C, Xu C, Gui C, Fox M (2005) Level set evolution without re-initialization: a new variational formulation. In: Proc. of IEEE conference on computer vision and pattern recognition. San Diego, pp 430–436Google Scholar
  13. 13.
    Lu SY (2016) Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl Sci 6(6), Article ID: 169Google Scholar
  14. 14.
    Lu H, Li Y (2017) Artificial intelligence and computer vision. Stud Comp Int Dev 672. doi:  10.1007/978-3-319-46245-5
  15. 15.
    Lu H, Li Y, Nakashima S, Kim H, Serikawa S (2017) Underwater image super-resolution by descattering and fusion. IEEE Access :1–9Google Scholar
  16. 16.
    Lu H, Li Y, Nakashima S, Yang S, Serikawa S (2012) A fast debris flow disasters areas detection method of earthquake images in remote sensing system. Disaster Adv 5(4):796–799Google Scholar
  17. 17.
    Lu H, Li Y, Nakashima S, Serikawa S (2016) Turbidity underwater image restoration using spectral properties and light compensation. IEICE Trans Inf Syst 99(1):219–227CrossRefGoogle Scholar
  18. 18.
    Lu H, Li Y, Nakashima S, Serikawa S (2016) Single image dehazing through improved atmospheric light estimation. Multimedia Tools Appl 75(24):17081–17096CrossRefGoogle Scholar
  19. 19.
    Lu H, Li Y, Xu X, Li J, Liu Z, Li X, Yang J, Serikawa S (2016) Underwater image enhancement method using weighted guided trigonometric filtering and artificial light correction. J Vis Commun Image Represent 38:504–516CrossRefGoogle Scholar
  20. 20.
    Lu H, Li B, Zhu J, Li Y, Li Y, Xu X, He L, Li X, Li J, Serikawa S (2016) Wound intensity correction and segmentation with convolutional neural networks. Concurr Comput: Pract Exp  10.1002/cpe.3927
  21. 21.
    Lu H, Lifeng Z, Seiichi S (2010) a method for infrared image segment based on sharp frequency localized contourlet transform and morphology. In: Proceeding of IEEE international conference on intelligent control and inform. Processing. Year of Publication: 2010 ISBN: 978-1-4244-7047-1, pp 79–82Google Scholar
  22. 22.
    Lu H, Serikawa S, Li Y, Zhang L, Yang S, Hu X (2012) Proposal of fast implicit level set scheme for medical image segmentation using the Chan and Vese model. Appl Mech Mater 103:695–699CrossRefGoogle Scholar
  23. 23.
    Lu H, Yujie L, Yuhki K, Lifeng Z, Seiichi S (2010) Using morphology methods to detect blood cancer cells. In: Proc. of the 5th international conference on soft computing and intelligent systems and the 13th international symposium on advanced intelligent systems. Year of Publication: 2010, pp 452–456.Google Scholar
  24. 24.
    Mumford D, Shah J (1989) Optimal approximation by piecewise smooth function and associated variational problems. Commun Pure Appl Math 42(5):577–685MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Peng B (2016) Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection. Sci Rep 6, Article ID: 21816Google Scholar
  26. 26.
    Phillips P (2015) Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging. Inf Sci 322:115–132MathSciNetCrossRefGoogle Scholar
  27. 27.
    Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRefGoogle Scholar
  28. 28.
    Sun Y (2016) A multilayer perceptron based smart pathological brain detection system by fractional Fourier entropy. J Med Syst 40(7), Article ID: 173Google Scholar
  29. 29.
    Tang X, Lin X, He L (2007) Research on automatic recognition system for leucocyte image. J Biomed Eng 24(6):1250–1255Google Scholar
  30. 30.
    Vese LA, Chan TF (2007) A multiphase level set framework for image segmentation using the Mumford-Shah model. Int J Comput Vis 50(3):271–293CrossRefMATHGoogle Scholar
  31. 31.
    Wu L (2011) Optimal multi-level thresholding based on maximum tsallis entropy via an artificial bee colony approach. Entropy 13(4):841–859CrossRefMATHGoogle Scholar
  32. 32.
    Zhang K, Song H, Zhang L (2010) Active contours driven by local image fitting energy. Pattern Recogn 43(4):1199–1206CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Yujie Li
    • 1
    • 2
  • Yun Li
    • 1
  • Hyoungseop Kim
    • 3
  • Seiichi Serikawa
    • 3
  1. 1.Yangzhou UniversityYangzhouChina
  2. 2.Chinese Academy of SciencesQingdaoChina
  3. 3.Kyushu Institute of TechnologyKitakyushuJapan

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