Skip to main content

Comparative Analysis of Pigment Network as a Feature for Melanoma Detection

  • Conference paper
  • First Online:
Intelligent Technologies and Applications (INTAP 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 932))

Included in the following conference series:

  • 1498 Accesses

Abstract

Perceiving and removing shades sorted out in dermoscopic pictures with the assistance of picture shading and geometry to break down Pigment Network as an element for Melanoma discovery. This examination is comprised of proposed philosophy. In the first one, computerized picture upgrade process will be done, permitting the age of an arrangement of guidelines, once connected concluded through picture, allow by development with veil through pixie contender near persist a piece’s color organize. Trendy another piece, Examination assemblies completed its veil will be completed; proposed system contains essential stages at planning stage specifically: preprocessing, key point recognizable proof and division settle besides and withdrawal assurance. Looking for those relating to the color system and making the analysis, regardless of whether it has shade arrange or not and furthermore producing the cover comparing to this example, assuming any. The strategy will be tried against a database of 200 pictures, which will demonstrate the unwavering quality of the strategies.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wiseman, M.: The second world cancer research fund/American Institute for Cancer Research expert report. Food, nutrition, physical activity, and the prevention of cancer: a global perspective: nutrition society and BAPEN medical symposium on ‘nutrition support in cancer therapy’. Proc. Nutr. Soc. 67(3), 253–256 (2008)

    Google Scholar 

  2. Jamil, U., Akram, M.U., Khalid, S., Abbas, S., Saleem, K.: Computer based melanocytic and nevus image enhancement and segmentation. BioMed Res. Int. 2016 (2016)

    Google Scholar 

  3. Islami, F., et al.: Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA: Cancer J. Clin. 68(1), 31–54 (2018)

    Google Scholar 

  4. Malvehy, J., et al.: Dermoscopy report: proposal for standardization: results of a consensus meeting of the International Dermoscopy Society. J. Am. Acad. Dermatol. 57(1), 84–95 (2007)

    Google Scholar 

  5. Psaty, E.L., Halpern, A.C.: Current and emerging technologies in melanoma diagnosis: the state of the art. Clin. Dermatol. 27(1), 35–45 (2009)

    Google Scholar 

  6. Jamil, U., et al.: Melanocytic and nevus lesion detection from diseased dermoscopic images using fuzzy and wavelet techniques. Soft Comput. 22(5), 1577–1593 (2018)

    Google Scholar 

  7. Goodson, A.G., Grossman, D.: Strategies for early melanoma detection: approaches to the patient with nevi. J. Am. Acad. Dermatol. 60(5), 719–735 (2009)

    Google Scholar 

  8. Guitera, P., Menzies, S.W.: State of the art of diagnostic technology for early-stage melanoma. Expert Rev. Anticancer Ther. 11(5), 715–723 (2011)

    Google Scholar 

  9. Celebi, M.E., et al.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31(6), 362–373 (2007)

    Google Scholar 

  10. Iyatomi, H., et al.: An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Comput. Med. Imaging Graph. 32(7), 566–579 (2008)

    Google Scholar 

  11. Alcón, J.F., et al.: Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis. IEEE J. Sel. Top. Signal Process. 3(1), 14–25 (2009)

    Google Scholar 

  12. Di Leo, G., et al.: Automatic diagnosis of melanoma: a software system based on the 7-point check-list. In: 2010 43rd Hawaii International Conference on System Sciences (HICSS). IEEE (2010)

    Google Scholar 

  13. Arroyo, J.L.G., Zapirain, B.G.: Automated detection of melanoma in dermoscopic images. In: Scharcanski, J., Celebi, M. (eds.) Computer Vision Techniques for the Diagnosis of Skin Cancer. Series in BioEngineering, pp. 298–306. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-39608-3_6

    Google Scholar 

  14. Mirzaalian, H., Lee, T.K., Hamarneh, G.: Learning features for streak detection in dermoscopic color images using localized radial flux of principal intensity curvature. In: 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). IEEE (2012)

    Google Scholar 

  15. Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.: Oriented pattern analysis for streak detection in dermoscopy images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 298–306. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_37

    Google Scholar 

  16. Sadeghi, M., et al.: Detection and analysis of irregular streaks in dermoscopic images of skin lesions. IEEE Trans. Med. Imaging 32(5), 849–861 (2013)

    Google Scholar 

  17. Alfed, N., Khelifi, F., Bouridane, A.: Improving a bag of words approach for skin cancer detection in dermoscopic images. In: 2016 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE (2016)

    Google Scholar 

  18. Alfed, N., et al.: Pigment network-based skin cancer detection. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2015)

    Google Scholar 

  19. Ballerini, L., Fisher, R.B., Aldridge, B., Rees, J.: A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: Celebi, M., Schaefer, G. (eds.) Color Medical Image Analysis. LNCVB, vol. 6, pp. 63–86. Springer, Dordrecht (2013). https://doi.org/10.1007/978-94-007-5389-1_4

    Google Scholar 

  20. Barata, C., Marques, J.S., Celebi, M.E.: Improving dermoscopy image analysis using color constancy. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE (2014)

    Google Scholar 

  21. Barata, C., Ruela, M., Mendonça, T., Marques, J.S.: A bag-of-features approach for the classification of melanomas in dermoscopy images: the role of color and texture descriptors. In: Scharcanski, J., Celebi, M. (eds.) Computer Vision Techniques for the Diagnosis of Skin Cancer. SERBIOENG, pp. 49–69. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-39608-3_3

    Google Scholar 

  22. Giotis, I., et al.: MED-NODE: a computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Syst. Appl. 42(19), 6578–6585 (2015)

    Google Scholar 

  23. Kruk, M., et al.: Melanoma recognition using extended set of descriptors and classifiers. EURASIP J. Image Video Process. 2015(1), 43 (2015)

    Google Scholar 

  24. Oliveira, R.B., et al.: A computational approach for detecting pigmented skin lesions in macroscopic images. Expert Syst. Appl. 61, 53–63 (2016)

    Google Scholar 

  25. Riaz, F., et al.: Detecting melanoma in dermoscopy images using scale adaptive local binary patterns. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2014)

    Google Scholar 

  26. Ruela, M., et al.: A system for the detection of melanomas in dermoscopy images using shape and symmetry features. Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. 5(2), 127–137 (2017)

    Google Scholar 

  27. Zhao, Y., et al.: Robust hashing for image authentication using Zernike moments and local features. IEEE Trans. Inf. Forensics Secur. 8(1), 55–63 (2013)

    Google Scholar 

  28. Abuzaghleh, O., Barkana, B.D., Faezipour, M.: Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. IEEE J. Transl. Eng. Health Med. 3, 1–12 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Uzma Jamil .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shafiq, U., Jamil, U., Ayub, N. (2019). Comparative Analysis of Pigment Network as a Feature for Melanoma Detection. In: Bajwa, I., Kamareddine, F., Costa, A. (eds) Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science, vol 932. Springer, Singapore. https://doi.org/10.1007/978-981-13-6052-7_63

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6052-7_63

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6051-0

  • Online ISBN: 978-981-13-6052-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics