Skip to main content
SpringerLink
Log in

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine

  • Research
  • Published:
Health Information Science and Systems Aims and scope Submit manuscript

Abstract

Purpose

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors.

Methods

In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM).

Results

The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features.

Conclusion

Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Diepgen TL, Mahler V. The epidemiology of skin cancer. Br J Dermatol. 2002;146(Suppl 61):1–6.

    Google Scholar 

  2. Rubin AI, Chen EH, Ratner D. Basal-cell carcinoma. N Engl J Med. 2005;353(21):2262–9.

    Article  CAS  PubMed  Google Scholar 

  3. Ganster H, Pinz P, Rohrer R, Wildling E, Binder M, Kittler H. Automated melanoma recognition. IEEE Trans Med Imaging. 2001;20(3):233–9.

    Article  CAS  PubMed  Google Scholar 

  4. Yuan, X, Yang Z, Zouridakis G, Mullani N. SVM-based texture classification and application to early melanoma detection. In: 28th annual international conference of the IEEE engineering in medicine and biology society, 2006 (EMBS’06), pp. 4775–4778. IEEE, New York; 2006.

  5. Lau HT, Al-Jumaily A. Automatically early detection of skin cancer: study based on neural network classification. In: International conference of IEEE soft computing and pattern recognition, 2009 (SOCPAR’09) (2009).

  6. Alcón JF, Ciuhu C, Kate WT, Heinrich A, Uzunbajakava N, Krekels G, Siem D, De Haan G. Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis. IEEE J Selected Topics Signal Process. 2009;3(1):14–25.

    Article  Google Scholar 

  7. Elgamal M. Automatic skin cancer images classification. IJACSA Int J Adv Comput Sci Appl. 2013;4(3):287–94.

    Google Scholar 

  8. Lee T, Ng V, Gallagher R, Coldman A, McLean D. “Dullrazor®: a software approach to hair removal from images. Comput Biol Med. 1997;27(6):533–43.

    Article  CAS  PubMed  Google Scholar 

  9. Fu K-S, Mui JK. A survey on image segmentation. Pattern Recogn. 1981;13(1):3–16.

    Article  Google Scholar 

  10. Chuang K-S, Tzeng H-L, Chen S, Wu J, Chen TJ. Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph. 2006;30(1):9–15.

    Article  PubMed  Google Scholar 

  11. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9(1):62–6.

    Article  Google Scholar 

  12. Zhang G, Chen S, Liao J. Otsu image segmentation algorithm based on morphology and wavelet transformation. In: 2011 3rd international conference on computer research and development (ICCRD), vol. 1. IEEE, New York; 2011.

  13. Chowdhury M, Hoque M, Khatun A. Image compression using discrete wavelet transform. IJCSI Int J Comput Sci Issues. 2012;9(4):327–30.

    Google Scholar 

  14. Wan J, Ren L, Zhao C. Image feature extraction based on the two-dimensional empirical mode decomposition. In: Congress on image and signal processing, 2008 (SP’08), vol. 1. IEEE, New York; 2008.

  15. Felsberg M, Sommer G. The monogenic signal. IEEE Trans Signal Process. 2001;49(12):3136–44.

    Article  Google Scholar 

  16. Weszka JS, Dyer CR, Rosenfeld A. A comparative study of texture measures for terrain classification. IEEE Trans Syst Man Cybern. 1976;4:269–85.

    Article  Google Scholar 

  17. Cortes C, Vapnik V. Support vector machine. Mach Learn. 1995;20(3):273–97.

    Google Scholar 

  18. Zien A, Rätsch G, Mika S, Schölkopf B, Lengauer T, Müller K-R. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics. 2000;16(9):799–807.

    Article  CAS  PubMed  Google Scholar 

  19. Zanaty EA. Support vector machines (SVMs) versus multilayer perception (MLP) in data classification. Egypt Inf J. 2012;13(3):177–83.

    Article  Google Scholar 

  20. Baitharu TR, Pani SK, Dhal SK. Comparison of Kernel selection for support vector machines using diabetes dataset. J Comput Sci Appl. 2016;3(6):181–4.

    Google Scholar 

  21. Mahmoud MKA, Al-Jumaily A, Maali Y, Anam K. Classification of malignant melanoma and benign nevi from skin lesions based on support vector machine. In: 2013 Fifth international conference on computational intelligence, modelling and simulation (CIMSim), pp. 236–241. IEEE, New York; 2013.

  22. Mahbod A, Ecker R, Ellinger I. Skin lesion classification using hybrid deep neural networks. arXiv preprint. arXiv:1702.08434 (2017).

  23. Siuly S, Li Y. Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach. Neural Comput Appl. 2015;26(4):799–811.

    Article  Google Scholar 

Download references

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Authors and Affiliations

  1. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Maram A. Wahba, Amira S. Ashour, Sameh A. Napoleon & Mustafa M. Abd Elnaby

  2. Department of Computer Science, University of Illinois at Springfield, Springfield, IL, USA

    Yanhui Guo

Authors
  1. Maram A. Wahba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amira S. Ashour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sameh A. Napoleon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mustafa M. Abd Elnaby
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yanhui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maram A. Wahba.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wahba, M.A., Ashour, A.S., Napoleon, S.A. et al. Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine. Health Inf Sci Syst 5, 10 (2017). https://doi.org/10.1007/s13755-017-0033-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13755-017-0033-x

Keywords

Search

Navigation