Pigment Network Detection and Analysis

  • Maryam Sadeghi
  • Paul Wighton
  • Tim K. Lee
  • David McLean
  • Harvey Lui
  • M. Stella Atkins
Chapter
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

We describe the importance of identifying pigment networks in lesions which may be melanomas, and survey methods for identifying pigment networks (PN) in dermoscopic images. We then give details of how machine learning can be used to classify images into three classes: PN Absent, Regular PN and Irregular PN.

Keywords

Dermoscopic structures Pigment network Melanoma Computer-aided diagnosis Machine learning Graph-based analysis 

References

  1. 1.
    Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 62(1), 10–29 (2012)Google Scholar
  2. 2.
    Siegel, R., Naishadham, D., Jemal, A.: Cancer facts and figures, pp. 1–68. American Cancer Society, Atlanta. http://www.cancer.org/research/cancerfactsfigures/acspc-031941 (2012). Accessed 3 Dec 2012
  3. 3.
    Koh, H.K., Miller, D.R., Geller, A.C., Clapp, R.W., Mercer, M.B., Lew, R.A.: Who discovers melanoma?: patterns from a population-based survey. J. Am. Acad. Dermatol. 26(6), 914–919 (1992)CrossRefGoogle Scholar
  4. 4.
    Stolz, W., Riemann, A., Cognetta, A.B., Pillet, L., Abmayr, W., Holzel, D., Bilek, P., Nachbar, F., Landthaler, M., Braun-Falco, O.: ABCD rule of dermatoscopy: a new practical method for early recognition of malignant melanoma. Eur. J. Dermatol. 4(7), 521–527 (1994)Google Scholar
  5. 5.
    Menzies, S.W., Ingvar, C., Crotty, K.A., et al.: Frequency and morphologic characteristics of invasive melanoma lacking specific surface microscopy features. Arch. Dermatol. 132, 1178–1182 (1996)CrossRefGoogle Scholar
  6. 6.
    Kenet, R., Fitzpatrick, T.: Reducing mortality and morbidity of cutaneous melanoma: a six year plan. b) identifying high and low risk pigmented lesions using epiluminescence microscopy. J. Dermatol. 21(11), 881–884 (1994)Google Scholar
  7. 7.
    Argenziano, G., Soyer, H.P., et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the internet. J. Am. Acad. Dermatol. 48(5), 679–693 (2003)CrossRefGoogle Scholar
  8. 8.
    Argenziano, G., Soyer, H.P., De Giorgio, V., Piccolo, D., Carli, P., Delfino, M., Ferrari, A., Hofmann-Wellenhof, V., Massi, D., Mazzocchetti, G., Scalvenzi, M., Wolf, I.H.: Interactive Atlas of Dermoscopy (Book and CD-ROM). Edra medical publishing and new media, Milan (2000)Google Scholar
  9. 9.
    Soyer, H.P., Argenziano, G., Chimenti, S., Menzies, S.W., Pehamberger, H., Rabinovitz, H.S., Stolz, W., Kopf, A.W.: Dermoscopy of Pigmented Skin Lesions: An Atlas Based on the Consensus Net Meeting on Dermoscopy 2000. Edra, Milan (2001)Google Scholar
  10. 10.
    Stanganelli, I.: Dermoscopy. http://emedicine.medscape.com/article/1130783-overview (2010). Accessed 12 May 2011
  11. 11.
    Celebi, M.E., Aslandogan, Y.A., Stoecker, W.V., Iyatomi, H., Oka, H., Chen, X.: Unsupervised border detection in dermoscopy images. Skin Res. Technol. 13(4), 454–462 (2007)CrossRefGoogle Scholar
  12. 12.
    Wighton, P., Sadeghi, M., Lee, T.K., Atkins, M.S.: A fully automatic random walker segmentation for skin lesions in a supervised setting. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 1108–1115 (2009)Google Scholar
  13. 13.
    Celebi, M.E., Iyatomi, H., Schaefer, G., Stoecker, W.V.: Lesion border detection in dermoscopy images. Comput. Med. Imaging Graph. 33(2), 148–153 (2009)CrossRefGoogle Scholar
  14. 14.
    Lee, T.K., McLean, D.I., Atkins, M.S.: Irregularity index: a new border irregularity measure for cutaneous melanocytic lesions. Med. Image Anal. 7(1), 47–64 (2003)CrossRefGoogle Scholar
  15. 15.
    Fleming, M.G., Steger, C., et al.: Techniques for a structural analysis of dermatoscopic imagery. Comput. med. imaging graph. 22(5), 375–389 (1998)CrossRefGoogle Scholar
  16. 16.
    Grana, C., Cucchiara, R., Pellacani, G., Seidenari, S.: Line detection and texture characterization of network patterns. In: Proceedings of 18th International Conference on Pattern Recognition, ICPR 2006, vol. 2, pp. 275–278. IEEE, Washington (2006)Google Scholar
  17. 17.
    Fischer, S., Guillod, J., et al.: Analysis of skin lesions with pigmented networks. In: Proceeding of International Conference Image Processing, pp. 323–326 (1996)Google Scholar
  18. 18.
    Anantha, M., Moss, R.H., Stoecker, W.V.: Detection of pigment network in dermatoscopy images using texture analysis. Comput. Med. Imaging Graph. 28(5), 225–234 (2004)CrossRefGoogle Scholar
  19. 19.
    Betta, G., Di Leo, G., Fabbrocini, G., Paolillo, A., Sommella, P.: Dermoscopic image-analysis system: estimation of atypical pigment network and atypical vascular pattern. In: IEEE International Workshop on Medical Measurement and Applications, pp. 63–67. IEEE Computer Society, Washington (2006)Google Scholar
  20. 20.
    Di Leo, G., Liguori, C., Paolillo, A., Sommella, P.: An improved procedure for the automatic detection of dermoscopic structures in digital ELM images of skin lesions. In: IEEE Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems, pp. 190–194 (2008)Google Scholar
  21. 21.
    Shrestha, B., Bishop, J., Kam, K., Chen, X., Moss, R.H., Stoecker, W.V., Umbaugh, S., Stanley, R.J., Celebi, M.E., Marghoob, A.A., et al.: Detection of atypical texture features in early malignant melanoma. Skin Res. Technol. 16(1), 60–65 (2010)CrossRefGoogle Scholar
  22. 22.
    Serrano, C., Acha, B.: Pattern analysis of dermoscopic images based on markov random fields. Pattern Recogn. 42(6), 1052–1057 (2009)CrossRefGoogle Scholar
  23. 23.
    Barata, C., Marques, J.S., Rozeira, J.: A system for the detection of pigment network in dermoscopy images using directional filters. IEEE Trans. Biomed. Eng. 59(10), 2744–2754 (2012)CrossRefGoogle Scholar
  24. 24.
    Nowak, L.A., Ogorzałek, M.J., Pawłowski, M.P.: Pigmented network structure detection using semi-smart adaptive filters. In: IEEE 6th International Conference on Systems Biology (ISB), pp. 310–314 (2012)Google Scholar
  25. 25.
    Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56(2), 69–90 (2012)CrossRefGoogle Scholar
  26. 26.
    Wighton, P., Lee, T.K., Lui, H., McLean, D.I., Atkins, M.S.: Generalizing common tasks in automated skin lesion diagnosis. IEEE Trans. Inf. Technol. Biomed. 15(4), 622–629 (2011)CrossRefGoogle Scholar
  27. 27.
    Sadeghi, M., Razmara, M., Ester, M., Lee, T.K., Atkins, M.S.: Graph-based pigment network detection in skin images. In: Proceeding of the SPIE Medical Imaging Conference, vol. 7623 (2010)Google Scholar
  28. 28.
    Sadeghi, M., Razmara, M., Lee, T.K., Atkins, M.S.: A novel method for detection of pigment network in dermoscopic images using graphs. Comput. Med. Imaging Graph. 35(2), 137–143 (2011)CrossRefGoogle Scholar
  29. 29.
    Sadeghi, M., Razmara, M., Wighton, P., Lee, T.K., Atkins, M.S.: Modeling the dermoscopic structure pigment network using a clinically inspired feature set. In: Medical Imaging and Augmented Reality, vol. 6326, pp. 467–474 (2010)Google Scholar
  30. 30.
    Pratt, W.K.: Digital Image Processing, 2nd edn. Wiley, New York (1991)Google Scholar
  31. 31.
    Kirk, J.: Count loops in a network. http://www.mathworks.com/matlabcentral/fx_files/10722/1/content/html/run_loops_html.html (2007). Accessed 12 May 2009
  32. 32.
    Shih, T.Y.: The reversibility of six geometric color spaces. Photogram. Eng. Remote Sens. 61(10), 1223–1232 (1995)Google Scholar
  33. 33.
    Haralick, R.M., Dinstein, I., Shanmugam, K.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  34. 34.
    Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59(1), 161–205 (2005)CrossRefMATHGoogle Scholar
  35. 35.
    Goebel, M.: A survey of data mining and knowledge discovery software tools. ACM Special Interest Group Knowl. Discov. Data Min. Explor. Newsl. 1(1), 20–33 (1999)MathSciNetGoogle Scholar
  36. 36.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28(2), 337–407 (2000)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Maryam Sadeghi
    • 1
    • 2
    • 3
  • Paul Wighton
    • 4
  • Tim K. Lee
    • 1
    • 2
    • 3
  • David McLean
    • 1
  • Harvey Lui
    • 1
  • M. Stella Atkins
    • 2
    • 1
  1. 1.Department of Dermatology and Skin ScienceUniversity of British ColumbiaVancouverCanada
  2. 2.School of Computing ScienceSimon Fraser UniversityVancouverCanada
  3. 3.Cancer Control Research ProgramBC Cancer Research CenterVancouverCanada
  4. 4.Martinos Center for Biomedical ImagingHarvard Medical SchoolBostonUSA

Personalised recommendations