Journal of Digital Imaging

, Volume 32, Issue 2, pp 322–335 | Cite as

Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering

  • S. N. KumarEmail author
  • A. Lenin Fred
  • P. Sebastin Varghese


Suspicious lesion or organ segmentation is a challenging task to be solved in most of the medical image analyses, medical diagnoses and computer diagnosis systems. Nevertheless, various image segmentation methods were proposed in the previous studies with varying success levels. But, the image segmentation problems such as lack of versatility, low robustness, high complexity and low accuracy in up-to-date image segmentation practices still remain unsolved. Fuzzy c-means clustering (FCM) methods are very well suited for segmenting the regions. The noise-free images are effectively segmented using the traditional FCM method. However, the segmentation result generated is highly sensitive to noise due to the negligence of spatial information. To solve this issue, super-pixel-based FCM (SPOFCM) is implemented in this paper, in which the influence of spatially neighbouring and similar super-pixels is incorporated. Also, a crow search algorithm is adopted for optimizing the influential degree; thereby, the segmentation performance is improved. In clinical applications, the SPOFCM feasibility is verified using the multi-spectral MRIs, mammograms and actual single spectrum on performing tumour segmentation tests for SPOFCM. Ultimately, the competitive, renowned segmentation techniques such as k-means, entropy thresholding (ET), FCM, FCM with spatial constraints (FCM_S) and kernel FCM (KFCM) are used to compare the results of proposed SPOFCM. Experimental results on multi-spectral MRIs and actual single-spectrum mammograms indicate that the proposed algorithm can provide a better performance for suspicious lesion or organ segmentation in computer-assisted clinical applications.


Mammograms Breast MR images Super-pixel Fuzzy C-means Spatial Clinical application 


Funding Information

This work is supported by DST under IDP scheme (No. IDP/MED/03/2015).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Informed Consent

Written informed consent was obtained from all patients included in the study.


  1. 1.
    Li C, Gore JC, Davatzikos C: Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn Reson Imaging 32(7):913–923, 2014CrossRefGoogle Scholar
  2. 2.
    Wang ZM, Soh YC, Song Q, Sim K: Adaptive spatial information-theoretic clustering for image segmentation. Pattern Recogn 42(9):2029–2044, 2009CrossRefGoogle Scholar
  3. 3.
    Tou JT, Gonzalez RC: Pattern recognition. Reading: Addison-Wesley, 1974Google Scholar
  4. 4.
    Modha DS, Spangler WS: Feature weighting in k-means clustering. Mach Learn 52(3):217–237, 2003CrossRefGoogle Scholar
  5. 5.
    Bezdek JC, Ehrlich R, Full W: FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2):191–203, 1984CrossRefGoogle Scholar
  6. 6.
    Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek JC: A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 3(5):672–682, 1992CrossRefGoogle Scholar
  7. 7.
    Krinidis S, Chatzis V: A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337, 2010CrossRefGoogle Scholar
  8. 8.
    Cai W, Chen S, Zhang D: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn 40(3):825–838, 2007CrossRefGoogle Scholar
  9. 9.
    Chuang K-S, Tzeng H-L, Chen S, Wu J, Chen T-J: Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15, 2006CrossRefGoogle Scholar
  10. 10.
    Pal NR, Pal K, Keller JM, Bezdek JC: A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530, 2005CrossRefGoogle Scholar
  11. 11.
    Yang X, Zhang G, Lu J, Ma J: A kernel fuzzy c-means clustering-based fuzzy support vector machine method for classification problems with outliers or noises. IEEE Trans Fuzzy Syst 19(1):105–115, 2011CrossRefGoogle Scholar
  12. 12.
    Le Capitaine H, Frelicot C: A cluster-validity index combining an overlap measure and a separation measure based on fuzzy-aggregation operators. IEEE Trans Fuzzy Syst 19(3):580–588, 2011CrossRefGoogle Scholar
  13. 13.
    Gong M, Zhou Z, Ma J: Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process 21(4):2141–2151, 2012CrossRefGoogle Scholar
  14. 14.
    Huang HC, Chuang YY, Chen CS: Multiple kernel fuzzy clustering. IEEE Trans Fuzzy Syst 20(1):120–134, 2012CrossRefGoogle Scholar
  15. 15.
    Balla-Arabe S, Gao X, Wang B: A fast and robust level set method for image segmentation using fuzzy clustering and lattice Boltzmann method. IEEE Trans Cybern 43(3):910–920, 2013CrossRefGoogle Scholar
  16. 16.
    Despotovic I, Vansteenkiste E, Philips W: Spatially coherent fuzzy clustering for accurate and noise-robust image segmentation. IEEE Signal Process Lett 20(4):295–298, 2013CrossRefGoogle Scholar
  17. 17.
    Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T: A modified fuzzy c-means method for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193–199, 2002CrossRefGoogle Scholar
  18. 18.
    Li X, Li L, Lu H, Chen D, Liang Z: In homogeneity correction for magnetic resonance images with fuzzy c-mean method. Proc SPIE Int Soc Opt Eng 5032:995–1005, 2003Google Scholar
  19. 19.
    Ng EKK, Fu AW-C, Wong RC-W: Projective clustering by histograms. IEEE Trans Knowl Data Eng 17(3):369–383, 2005CrossRefGoogle Scholar
  20. 20.
    Li B, Chen W, Wang D: An improved FCM method incorporating spatial information for image segmentation. In: Proc of International Symposium on Computer Science and Computational Technology, 2008, pp 493–495Google Scholar
  21. 21.
    Chen S, Zhang D: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern B 34(4):1907–1916, 2004CrossRefGoogle Scholar
  22. 22.
    Kannan SR, Ramathilagam S, Devi R, Sathya A: Robust kernel FCM in segmentation of breast medical images. Expert Syst Appl 38(4):4382–4389, 2011CrossRefGoogle Scholar
  23. 23.
    Liapis S, Sifakis E, Tziritas G: Colour and texture segmentation using wavelet frame analysis, deterministic relaxation, and fast marching methods. J Vis Commun Image Represent 15:1–26, 2004CrossRefGoogle Scholar
  24. 24.
    Yu H, Zhang X, Wang S, Hou B: Context-based hierarchical unequal merging for SAR image segmentation. IEEE Trans Geosci Remote Sens 51(2):995–1009, 2013CrossRefGoogle Scholar
  25. 25.
    Sundararaj V, Muthukumar S, Kumar RS: An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288, 2018Google Scholar
  26. 26.
    Sundararaj V: Optimal task assignment in mobile cloud computing by queue based Ant-Bee algorithm. Wirel Pers Commun 1–25, 2018.
  27. 27.
    Sundararaj V: An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126, 2016Google Scholar
  28. 28.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P: Sabine, SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282, 2012CrossRefGoogle Scholar
  29. 29.
    Shotton J, Johnson M, Cipolla R: Semantic texton forests for image categorization and segmentation. In: European Conference on Computer Vision, 2008, pp 1–8Google Scholar
  30. 30.
    Madhulatha TS: An Overview on Clustering Methods. IOSR J Eng 2(4):719–725, 2012CrossRefGoogle Scholar
  31. 31.
    Ke J, Hall LO, Goldgof DB: Fast accurate fuzzy clustering through data reduction. IEEE Trans Fuzzy Syst 11(2):262–270, 2003CrossRefGoogle Scholar
  32. 32.
    Hemanth DJ, Selvathi D, Anitha J: Effective Fuzzy Clustering Method for Abnormal MR Brain Image Segmentation. In: IEEE International Advance Computing Conference, 2009, pp 609–614Google Scholar
  33. 33.
    Askarzadeh A: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12, 2016CrossRefGoogle Scholar
  34. 34.
    Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S, Taylor P: “The mammographic image analysis society digital mammogram database,” In Exerpta Medica International Congress Series, Vol. 1069, 1994, pp 375–378Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  • S. N. Kumar
    • 1
    Email author
  • A. Lenin Fred
    • 2
  • P. Sebastin Varghese
    • 3
  1. 1.Department of ECESathyabama Institute of Science and TechnologyChennaiIndia
  2. 2.School of CSEMar Ephraem College of Engineering and TechnologyMarthandamIndia
  3. 3.Metro Scans & LaboratoryTrivandrumIndia

Personalised recommendations