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Image Enhancement Based on Fractional Poisson for Segmentation of Skin Lesions Using the Watershed Transform

  • Alaa Ahmed Abbas Al-abayechi
  • Hamid A. JalabEmail author
  • Rabha W. Ibrahim
  • Ali M. Hasan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)

Abstract

Image segmentation is considered as a necessary step towards accurate medical analysis by extracting the crucial medical information in identifying abnormalities. This study proposes a new technique for segmentation a malignant melanoma in images. A new filter is proposed for smoothing input images and more accurate segmentation based on fractional Poisson. In the pre-processing step, eight masks of size n × n are created to eliminate noise and obtain a smooth image. The watershed algorithm is used for segmentation with morphological operation to better segment the skin lesion area. The proposed method was capable of improving the accuracy of the segmentation up to 96.47%.

Keywords

Fractional calculus Fractional Poisson Watershed algorithm Skin lesion Segmentation 

Notes

Acknowledgements

The authors would like to thank both Dr. Joaquim M. da Cunha Viana and Mr. Navid Razmjooy for providing the dermoscopic images used in this study. We would also like to thank skin specialist Dr. Mohammed Ahmed, for providing the necessary information for this study. This research is supported by the Fundamental Research Grant Scheme (FRGS), Project: FP073-2015A from Ministry of Higher Education, Malaysia.

References

  1. 1.
    Von Landesberger, T., Andrienko, G., Andrienko, N., Bremm, S., Kirschner, M., Wesarg, S., Kuijper, A.: Opening up the “black box” of medical image segmentation with statistical shape models. Vis. Comput. 29, 893–905 (2013)CrossRefGoogle Scholar
  2. 2.
    Wu, Q., Merchant, F., Castleman, K.: Microscope Image Processing. Academic Press, Cambridge (2010)Google Scholar
  3. 3.
    Garnavi, R., Aldeen, M., Celebi, M.E., Varigos, G., Finch, S.: Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Comput. Med. Imaging Graph. 35, 105–115 (2011)CrossRefGoogle Scholar
  4. 4.
    Jung, C.R.: Multiscale image segmentation using wavelets and watersheds. In: 2003 XVI Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI 2003, pp. 278–284. IEEE (2003)Google Scholar
  5. 5.
    Haris, K., Efstratiadis, S.N., Maglaveras, N., Katsaggelos, A.K.: Hybrid image segmentation using watersheds and fast region merging. IEEE Trans. Image Process. 7, 1684–1699 (1998)CrossRefGoogle Scholar
  6. 6.
    Weickert, J.: Efficient image segmentation using partial differential equations and morphology. Pattern Recogn. 34, 1813–1824 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Ahmed Abbas, A., Tan, W.-H., Guo, X.-N.: Combined optimal wavelet filters with morphological watershed transform for the segmentation of dermoscopic skin lesions. In: Anthony, P., Ishizuka, M., Lukose, D. (eds.) PRICAI 2012. LNCS, vol. 7458, pp. 722–727. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32695-0_63 CrossRefGoogle Scholar
  8. 8.
    Mustaqeem, A., Javed, A., Fatima, T.: An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. Int. J. Image Graph. Sig. Process. 4, 34 (2012)CrossRefGoogle Scholar
  9. 9.
    Hamarneh, G., Li, X.: Watershed segmentation using prior shape and appearance knowledge. Image Vis. Comput. 27, 59–68 (2009)CrossRefGoogle Scholar
  10. 10.
    Wang, H., Moss, R.H., Chen, X., Stanley, R.J., Stoecker, W.V., Celebi, M.E., Malters, J.M., Grichnik, J.M., Marghoob, A.A., Rabinovitz, H.S.: Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images. Comput. Med. Imaging Graph. 35, 116–120 (2011)CrossRefGoogle Scholar
  11. 11.
    Jalab, H.A., Ibrahim, R.W.: Fractional Alexander polynomials for image denoising. Sig. Process. 107, 340–354 (2015)CrossRefGoogle Scholar
  12. 12.
    Jalab, H.A., Ibrahim, R.W.: Texture enhancement based on the Savitzky-Golay fractional differential operator. Math. Probl. Eng. 2013, 1–8 (2013)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Ibrahim, R.W., Jalab, H.A.: Existence of entropy solutions for nonsymmetric fractional systems. Entropy 16, 4911–4922 (2014)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Ibrahim, R.W., Jalab, H.A.: Existence of ulam stability for iterative fractional differential equations based on fractional entropy. Entropy 17, 3172–3181 (2015)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Laskin, N.: Fractional poisson process. Commun. Nonlinear Sci. Numer. Simul. 8, 201–213 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Stoev, S.L.: A fast watershed algorithm based on rainfalling simulation (2000)Google Scholar
  17. 17.
    Sarker, M.S.Z., Haw, T.W., Logeswaran, R.: Morphological based technique for image segmentation. Int. J. Inf. Technol. 14, 55–80 (2008)Google Scholar
  18. 18.
    Logeswaran, R., Haw, T.W., Sarker, S.Z.: Liver isolation in abdominal MRI. J. Med. Syst. 32, 259–268 (2008)CrossRefGoogle Scholar
  19. 19.
    Schaefer, G., Rajab, M.I., Celebi, M.E., Iyatomi, H.: Colour and contrast enhancement for improved skin lesion segmentation. Comput. Med. Imaging Graph. 35, 99–104 (2011)CrossRefGoogle Scholar
  20. 20.
    Wu, Y., Xie, F., Jiang, Z., Meng, R.: Automatic skin lesion segmentation based on supervised learning. In: 2013 Seventh International Conference on Image and Graphics (ICIG), pp. 164–169. IEEE (2013)Google Scholar
  21. 21.
    Zhou, H., Schaefer, G., Celebi, M.E., Lin, F., Liu, T.: Gradient vector flow with mean shift for skin lesion segmentation. Comput. Med. Imaging Graph. 35, 121–127 (2011)CrossRefGoogle Scholar
  22. 22.
    He, Y., Xie, F.: Automatic skin lesion segmentation based on texture analysis and supervised learning. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7725, pp. 330–341. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37444-9_26 CrossRefGoogle Scholar
  23. 23.
    Cavalcanti, P.G., Yari, Y., Scharcanski, J.: Pigmented skin lesion segmentation on macroscopic images. In: 2010 25th International Conference of Image and Vision Computing New Zealand (IVCNZ), pp. 1–7. IEEE (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alaa Ahmed Abbas Al-abayechi
    • 1
  • Hamid A. Jalab
    • 2
    Email author
  • Rabha W. Ibrahim
    • 3
  • Ali M. Hasan
    • 4
  1. 1.Al-Rusafa of Management InstituteMiddle Technical UniversityBaghdadIraq
  2. 2.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  3. 3.Modern College of Business and ScienceMuscatOman
  4. 4.School of Computing, Science and EngineeringUniversity of SalfordManchesterUK

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