HSL Color Space Based Skin Lesion Segmentation Using Fuzzy-Based Techniques

  • P. GanesanEmail author
  • B. S. Sathish
  • L. M. I. Leo Joseph
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 665)


Skin lesion is the anomalous intensification contrast to the skin just about it. It is categorized as primary or secondary. The primary lesions are uncharacteristic skin circumstances existence at birth. The secondary lesions are the result of manipulated primary lesions. There are more than 20 types of skin lesions. Segmentation is the process of partition of the test image into number of significant clusters. Every cluster should be unique in terms of any one of the image attributes such as texture, intensity, or color. The accomplishment of image analysis primarily based on the upshot of the segmentation process. The proposed approach performs the skin lesion segmentation using fuzzy c-means clustering (FCM), Possibilistic c-means clustering (PCM). Possibilistic fuzzy c-means clustering (PFCM) and modified fuzzy c-means clustering (PFCM). The experimental result reveals the competency of the MFCM for skin lesion segmentation.


Segmentation Color space Skin lesion Clustering Fuzzy c-means clustering 


  1. 1.
    Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., Nandi, A.K.: Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans. Fuzzy Syst. 26(5), 3027–3041 (2018)CrossRefGoogle Scholar
  2. 2.
    Ganesan, P., Sajiv, G.: User oriented color space for satellite image segmentation using fuzzy based techniques. In: International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–6 (2017)Google Scholar
  3. 3.
    Pal, NR., Pal, K., James Bezdek, A.: A possibilistic fuzzy C means clustering algorithm. IEEE Trans. Fuzzy Syst. 13(4), 517–530 (2005)Google Scholar
  4. 4.
    Ganesan, P., Palanivel, K., Sathish, B.S., Kalist, V., Shaik, K.B.: Performance of fuzzy based clustering algorithms for the segmentation of satellite images—A comparative study. In: IEEE Seventh National Conference on Computing, Communication and Information Systems (NCCCIS), pp. 23–27 (2015)Google Scholar
  5. 5.
    Krinidis, S., Chatzis, V.: A robust fuzzy local information C-means clustering algorithm. IEEE Trans. Image Process. 19(5), 1328–1337 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Sathish, B.S., Ganesan, P., Shaik, K.B.: Color Image segmentation based on genetic algorithm and histogram threshold. Int. J. Appl. Eng. Res. 10(6), 5205–5209 (2015)Google Scholar
  7. 7.
    Chuang, K.S., Tzeng, H.L., Wu, J., Chen, T.J.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Image Graph. 30(1), 9–15 (2006)CrossRefGoogle Scholar
  8. 8.
    Krishnapuram, R., Keller, J.: The possibilistic c means algorithm: Insights and recommendations. IEEE Trans. Fuzzy Syst. 1(2), 98–110 (1993)CrossRefGoogle Scholar
  9. 9.
    Correa, C.: A comparison of fuzzy clustering algorithms applied to feature extraction on vineyard. Lecture Notes in Computer Science, pp. 56–65. Springer, Berlin (2012)Google Scholar
  10. 10.
    Ganesan, P., Rajini, V.: Assessment of satellite image segmentation in RGB and HSV color space using image quality measures. In: International Conference on Advances in Electrical Engineering (ICAEE), pp. 1–5 (2014)Google Scholar
  11. 11.
    Kalist, V., Ganesan, P., Sathish, B.S., Jenitha, J.M.M.: possibilistic-fuzzy C-means clustering approach for the segmentation of satellite images in HSL color space. Procedia Comput. Sci. 57, 49–56 (2015)Google Scholar
  12. 12.
    Sajiv, G., Ganesan, P.: Comparative study of possibilistic fuzzy C-means clustering based image segmentation in RGB and CIELuv color space. Int. J. Pharm. Technol. 8(1), 10899–10909 (2016)Google Scholar
  13. 13.
    Ganesan, P., Sathish, B.S., Sajiv, G: A comparative approach of identification and segmentation of forest fire region in high resolution satellite images. In: 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), pp. 1–6 (2016)Google Scholar
  14. 14.
    Ganesan, P., Sajiv, G.: Unsupervised clustering of satellite images in CIELab color space using spatial information incorporated FCM clustering method. Int. J. Appl. Eng. Res. 10(20), 18774–18780 (2015)Google Scholar
  15. 15.
    Wang, Z., Song, Q., Soh, Y.C., Sim, K.: An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation. Comput. Vis. Image Understand. 117, 1412–1420 (2013)CrossRefGoogle Scholar
  16. 16.
    Havens, T., Bezdek, J.C., Leckie, C., Hall, L.O., Palaniswami, M.: Fuzzy c-means algorithms for very large data. IEEE Trans. Fuzzy Syst. 20(6), 1130–1146 (2012)CrossRefGoogle Scholar
  17. 17.
    Jia, S., Zhang, C.: Fast and robust image segmentation using an superpixel based FCM algorithm. In: IEEE International Conference on Image Processing (ICIP), pp. 947–951 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • P. Ganesan
    • 1
    Email author
  • B. S. Sathish
    • 2
  • L. M. I. Leo Joseph
    • 3
  1. 1.Department of Electronics and Communication EngineeringVidya Jyothi Institute of TechnologyHyderabadIndia
  2. 2.Department of Electronics and Communication EngineeringRamachandra College of EngineeringEluruIndia
  3. 3.Department of Electronics and Communication EngineeringS.R. Engineering CollegeWarangalIndia

Personalised recommendations