A Bag-of-Features Approach for the Classification of Melanomas in Dermoscopy Images: The Role of Color and Texture Descriptors

  • Catarina Barata
  • Margarida Ruela
  • Teresa Mendonça
  • Jorge S. Marques
Chapter
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

The identification of melanomas in dermoscopy images is still an up to date challenge. Several Computer Aided-Diagnosis Systems for the early diagnosis of melanomas have been proposed in the last two decades. This chapter presents an approach to diagnose melanomas using Bag-of-features, a classification method based on a local description of the image in small patches. Moreover, a comparison between color and texture descriptors is performed in order to assess their discriminative power. The presented results show that local descriptors allow an accurate representation of dermoscopy images and achieve good classification scores: Sensitivity \(=\) 93 % and Specificity \(=\) 88 %. Furthermore it shows that color descriptors perform better than texture ones in the detection of melanomas.

Keywords

Melanoma diagnosis Dermoscopy Bag-of-features  Feature extraction Feature analysis Color features  Texture features 

Notes

Acknowledgments

The authors thank to Dr. Jorge Rozeira for providing the dermoscopy images. This work was supported by Fundação Ciência e Tecnologia in the scope of the grant SFRH/BD/84658/2012 and projects PTDC/SAU-BEB/103471/2008 and PEst-OE/EEI/LA0009/2011.

References

  1. 1.
    Abbas, Q., Celebi, M.E., Serrano, C., García, I.F., Ma, G.: Pattern classification of dermoscopy images: a perceptually uniform model. Pattern Recogn. 46, 86–97 (2013)CrossRefGoogle Scholar
  2. 2.
    Argenziano, G., Fabbrocini, G., Carli, P., De Giorgi, V., Sammarco, E., Delfino, M.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch. Dermatol. 134, 1563–1570 (1998)CrossRefGoogle Scholar
  3. 3.
    Argenziano, G., Soyer, H., De Giorgi, V., Carli, P., Delfino, M., Ferrari, A., Hofmann-Wellenhof, R., Massi, D., Mazocchetti, G., Scalvenzi, M., Wolf, I.: Interactive atlas of dermoscopy. Edra Medical Publishing and New Media, Milan (2000). http://www.dermoscopy.org/atlas/
  4. 4.
    Arivazhagana, S., Ganesanb, L., Priyala, S.P.: Texture classification using gabor wavelets based rotation invariant features. Pattern Recogn. Lett. 27, 1976–1982 (2006)CrossRefGoogle Scholar
  5. 5.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, New York (1999)Google Scholar
  6. 6.
    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
  7. 7.
    Bratkova, M., Boulos, S., Shirley, P.: oRGB: A pratical opponent color space for computer graphics. IEEE Comput. Graphics Appl. 29, 42–55 (2009)CrossRefGoogle Scholar
  8. 8.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2, 121–167 (1998)CrossRefGoogle Scholar
  9. 9.
    Celebi, M.E., Kingravi, H.E., Uddin, B., Iyatomi, H., Aslandogan, Y., Stoecker, W.V., Moss, R.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imag. Graphics 31(6), 362–373 (2007)Google Scholar
  10. 10.
    Celebi, M.E., Iyatomi, H., Stoecker, W., Moss, R.H., Rabinovitz, H., Soyer, H.P.: Automatic detection of blue-white veil and related structures in dermoscopy images. Comput. Med. Imag. Graph. 32(8), 670–677 (2008)CrossRefGoogle Scholar
  11. 11.
    Celebi, M.E., Stoecker, W.V., Moss, R.H.: Advances in skin cancer image analysis. Comput. Med. Imag. Graph. 35, 83–84 (2011)CrossRefGoogle Scholar
  12. 12.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
  13. 13.
    Clausi, D.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. 28(1), 45–62 (2002)CrossRefGoogle Scholar
  14. 14.
    Dala, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and. Pattern Recogn. 1, 886–893 (2005)Google Scholar
  15. 15.
    Di Leo, G., Paolillo, A., Sommella, P., Fabbrocini, G.: Automatic diagnosis of melanoma: a software system based on the 7-point check-list. In: Proceedings of the 2010 43rd Hawaii International Conference on System Sciences, pp. 1818–1823 (2010)Google Scholar
  16. 16.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (1999)Google Scholar
  17. 17.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Ganster, H., Pinz, A., Wildling, E., Binder, M., Kittler, H.: Automated melanoma recognition. IEEE Trans. Med. Imag. 20(3), 233–239 (2001)CrossRefGoogle Scholar
  19. 19.
    Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour detection based on nonclassical receptive field inhibition. IEEE Trans. Image Process. 12, 729–739 (2003)CrossRefGoogle Scholar
  20. 20.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  21. 21.
    Iyatomi, H., Oka, H., Celebi, M.E., Hashimoto, M., Hagiwara, M., Tanaka, M., Ogawa, K.: An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Comput. Med. Imag. Graphics 32(7), 566–579 (2008)CrossRefGoogle Scholar
  22. 22.
    Jiang, Y.G., Ngo, C.W., Yang, J.: Towards optimal bag-of-features for object categorization and semantic video retrieval. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 494–501 (2007)Google Scholar
  23. 23.
    Khan, F.S., van de Weijer, J., Vanrell, M.: Top-down color attention for object recognition. In: Proceedings of the IEEE 12th International Conference on Computer Vision, pp. 979–986 (2009)Google Scholar
  24. 24.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20, 226–239 (1998)CrossRefGoogle Scholar
  25. 25.
    Korotkov, K., Garcia, R.: Computerized analysis of pigment skin lesions: a review. Artif. Intell. Med. 56, 69–90 (2012)CrossRefGoogle Scholar
  26. 26.
    Laws, K.I.: Rapid texture identification. In: Proceedings of SPIE Conference on Image Processing for Missile Guidance (1980)Google Scholar
  27. 27.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)Google Scholar
  28. 28.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  29. 29.
    Menzies, S., Ingvar, C., Crotty, K., McCarthy, W.H.: Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. Arch. Dermatol. 132, 1178–1182 (1996)CrossRefGoogle Scholar
  30. 30.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)CrossRefGoogle Scholar
  31. 31.
    Pellacani, G., Grana, C., Cucchiara, R., Seidenari, S.: Automated extraction of dark areas in surface microscopy melanocytic lesion images. Dermatology 208(1), 21–26Google Scholar
  32. 32.
    Platt, J.: Probabilities for sv machines. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (2000)Google Scholar
  33. 33.
    Randen, T., Husoy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21, 291–310 (1999)CrossRefGoogle Scholar
  34. 34.
    Rubegni, P., Cevenini, G., Burroni, M., Perotti, R., Dell’Eva, G., Sbano, P., Miracco, C.: Automated diagnosis of pigmented skin lesions. Int. J. Cancer 101(6), 576–580 (2002)CrossRefGoogle Scholar
  35. 35.
    Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.: Detection and analysis of irregular streaks in dermoscopic images of skin lesions. IEEE Trans. Med. Imag. 32(5), 849–861. doi: 10.1109/TMI.2013.2239307
  36. 36.
    Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.: Global pattern analysis and classification of dermoscopic images using textons. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, pp. 168–173 (2012)Google Scholar
  37. 37.
    Sadeghi, M., Razmara, M., Wighton, P., Lee, T.K., Atkins, M.S.: A novel method for detection of pigment network in dermoscopic images using graphs. Comput. Med. Imag. Graph. 35(2), 137–143 (2011)CrossRefGoogle Scholar
  38. 38.
    Van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: A comparison of color features for visual concept classification. In: Proceedings of the 2008 international conference on Content-based image and video retrieval (2008)Google Scholar
  39. 39.
    van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1582–1593 (2010)CrossRefGoogle Scholar
  40. 40.
    Serrano, C., Acha, B.: Pattern analysis of dermoscopic images based on markov random fields. Pattern Recogn. 42, 1052–1057 (2009)CrossRefGoogle Scholar
  41. 41.
    Silveira, M., Nascimento, J.C., Marques, J.S., Marçal, A.R.S., Mendonça, T., Yamauchi, S., Maeda, J.: Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J. Sel. Top. Sign. Process. 3, 35–45 (2009)CrossRefGoogle Scholar
  42. 42.
    Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proceedings of the 9th IEEE International Conference on Computer Vision, pp. 1470–1477 (2003)Google Scholar
  43. 43.
    Snoek, C.G.M.: Early versus late fusion in semantic video analysis. In: In ACM Multimedia, pp. 399–402 (2005)Google Scholar
  44. 44.
    Squire, D.M., Mller, W., Mller, H., Raki, J.: Content-based query of image databases, inspirations from text retrieval: inverted files, frequency-based weights and relevance feedback. Pattern Recogn. Lett. 21(13–14), 143–149 (1999)Google Scholar
  45. 45.
    Stoecker, W.V., Gupta, K., Stanley, R.J., et al.: Detection of asymmetric blotches in dermoscopy images of malignant melanomas using relative color. Skin Res. Technol. 11(3), 179–184 (2005)CrossRefGoogle Scholar
  46. 46.
    Stoecker, W.V., Wronkiewicz, M., Chowdhury, R., Stanley, R., Xu, J., Bangert, A., Shrestha, B., Calcara, D., Rabinovitz, H., Oliviero, M., Ahmed, F., Perry, L., Drugge, R.: Detection of granularity in dermoscopy images of malignant melanoma using color and texture features. Comput. Med. Imag. Graph. 35(2), 144–147 (2011)CrossRefGoogle Scholar
  47. 47.
    Stolz, W., Riemann, A., Cognetta, A.B.: ABCD rule of dermatoscopy: a new practical method for early recognition of malignant melanoma. Eur. J. Dermatol. 4, 521–527 (1994)Google Scholar
  48. 48.
    Stricker, M., Orengo, M.: Similarity of color images. In: Proceedings SPIE, vol. 2420, pp. 381–392 (1995)Google Scholar
  49. 49.
    Tkalcic, M., Tasicl, J.F.: Colour spaces: perceptual, historical and applicational background. In: Proceedings of the IEEE Region 8 EUROCON 2003. Computer as a Tool, vol. 1, pp. 304–308 (2003)Google Scholar
  50. 50.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: An in-depth study. Tech. Rep. 5737, Institut National De Recherche en Informatique et en Automatique (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Catarina Barata
    • 1
  • Margarida Ruela
    • 1
  • Teresa Mendonça
    • 2
  • Jorge S. Marques
    • 1
  1. 1.Institute for Systems and RoboticsInstituto Superior TécnicoLisboaPortugal
  2. 2.Faculdade de Ciências da Universidade do PortoPortoPortugal

Personalised recommendations