A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions

  • Lucia Ballerini
  • Robert B. Fisher
  • Ben Aldridge
  • Jonathan Rees
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 6)


This chapter proposes a novel hierarchical classification system based on the K-Nearest Neighbors (K-NN) model and its application to non-melanoma skin lesion classification. Color and texture features are extracted from skin lesion images. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is embedded in the hierarchical framework that chooses the most relevant feature subsets at each node of the hierarchy. The accuracy of the proposed hierarchical scheme is higher than 93 % in discriminating cancer and potential at risk lesions from benign lesions, and it reaches an overall classification accuracy of 74 % over five common classes of skin lesions, including two non-melanoma cancer types. This is the most extensive known result on non-melanoma skin cancer classification using color and texture information from images acquired by a standard camera (non-dermoscopy).


Feature Selection Classification Accuracy Texture Feature Basal Cell Carcinoma Color Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank the Wellcome Trust for funding this project (Grant No: 083928/Z/07/Z).


  1. 1.
    Alcón JF, Heinrich A, Uzunbajakava N, Krekels G, Siem D, de Haan G, de Haan G (2009) Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis. IEEE J Sel Top Signal Process 3:14–25 CrossRefGoogle Scholar
  2. 2.
    Aldridge RB, Glodzik D, Ballerini L, Fisher RB, Rees JL (2011) The utility of non-rule-based visual matching as a strategy to allow novices to achieve skin lesion diagnosis. Acta Derm-Venereol 91:279–283 CrossRefGoogle Scholar
  3. 3.
    Aldridge RB, Li X, Ballerini L, Fisher RB, Rees JL (2010) Teaching dermatology using 3-dimensional virtual reality. Archives of Dermatology 149(10) Google Scholar
  4. 4.
    Aldridge RB, Zanotto M, Ballerini L, Fisher RB, Rees JL (2011) Novice identification of melanoma: not quite as straightforward as the ABCDs. Acta Derm-Venereol 91:125–130 Google Scholar
  5. 5.
    Armengol E (2011) Classification of melanomas in situ using knowledge discovery with explained case-based reasoning. Artif Intell Med 51:93–105 CrossRefGoogle Scholar
  6. 6.
    Arvis V, Debain C, Berducat M, Benassi A (2004) Generalization of the cooccurence matrix for colour images: application to colour texture classification. Image Anal Stereol 23(1):63–72 CrossRefGoogle Scholar
  7. 7.
    Aslandogan Y, Mahajani G (2004) Evidence combination in medical data mining. In: Proceedings of international conference on information technology: coding and computing, vol 2, pp 465–469 CrossRefGoogle Scholar
  8. 8.
    Ballerini L, Li X, Fisher RB, Aldridge B, Rees J (2010) Content-based image retrieval of skin lesions by evolutionary feature synthesis. In: di Chio C, et al. (eds) Application of evolutionary computation, Istanbul, Turkey. Lectures notes in computer science, vol 6024, pp 312–319 CrossRefGoogle Scholar
  9. 9.
    Ballerini L, Li X, Fisher RB, Rees J (2010) A query-by-example content-based image retrieval system of non-melanoma skin lesions. In: Caputo B (ed) Proceedings MICCAI-09 workshop MCBR-CDS 2009: medical content-based retrieval for clinical decision support. LNCS, vol 5853. Springer, Berlin, pp 31–38 CrossRefGoogle Scholar
  10. 10.
    Basarab T, Munn S, Jones RR (1996) Diagnostic accuracy and appropriateness of general practitioner referrals to a dermatology out-patient clinic. Br J Dermatol 135(1):70–73 CrossRefGoogle Scholar
  11. 11.
    Cascinelli N, Ferrario M, Tonelli T, Leo E (1987) A possible new tool for clinical diagnosis of melanoma: the computer. J Am Acad Dermatol 16(2):361–367 CrossRefGoogle Scholar
  12. 12.
    Cavalcanti PG, Scharcanski J (2011) Automated prescreening of pigmented skin lesions using standard cameras. Comput Med Imaging Graph 35(6):481–491 CrossRefGoogle Scholar
  13. 13.
    Ceci M, Malerba D (2003) Hierarchical classification of HTML documents with WebClassII. In: Proceedings of the 25th European conference on information retrieval, pp 57–72 Google Scholar
  14. 14.
    Celebi ME, Iyatomi H, Schaefer G, Stoecker WV (2009) Lesion border detection in dermoscopy images. Comput Med Imaging Graph 33(2):148–153 CrossRefGoogle Scholar
  15. 15.
    Celebi ME, Kingravi HA, Uddin B, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH (2007) A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph 31(6):362–373 CrossRefGoogle Scholar
  16. 16.
    Celebi ME, Stoecker WV, Moss RH (2011) Advances in skin cancer image analysis. Comput Med Imaging Graph 35(2):83–84 CrossRefGoogle Scholar
  17. 17.
    Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27 zbMATHCrossRefGoogle Scholar
  18. 18.
    Cancer research UK (CRUK). CancerStats, Internet (2011). URL Accessed 03/08/2011
  19. 19.
    Dalal A, Moss RH, Stanley RJ, Stoecker WV, Gupta K, Calcara DA, Xu J, Shrestha B, Drugge R, Malters JM, Perry LA (2011) Concentric decile segmentation of white and hypopigmented areas in dermoscopy images of skin lesions allows discrimination of malignant melanoma. Comput Med Imaging Graph 35(2):148–154 CrossRefGoogle Scholar
  20. 20.
    D’Alessio S, Murray K, Schiaffino R, Kershenbaum A (2000) The effect of using hierarchical classifiers in text categorization. In: Proceedings of 6th international conference recherche d’information assistee par ordinateur, pp 302–313 Google Scholar
  21. 21.
    Day GR, Barbour RH (2000) Automated melanoma diagnosis: where are we at? Skin Res Technol 6:1–5 CrossRefGoogle Scholar
  22. 22.
    Dimitrovski I, Kocev D, Loskovska S, Dzeroski S (2011) Hierarchical annotation of medical images. Pattern Recognit 44(10–11):2436–2449 CrossRefGoogle Scholar
  23. 23.
    Dumais S, Chen H (2000) Hierarchical classification of web content. In: Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 256–263 Google Scholar
  24. 24.
    Duwairi R, Al-Zubaidi R (2011) A hierarchical K-NN classifier for textual data. Int Arab J Inf Technol 8(3):251–259 Google Scholar
  25. 25.
    Fix E, Hodges JL (1989) Discriminatory analysis. Nonparametric discrimination: consistency properties. Int Stat Rev 57(3):238–247 zbMATHCrossRefGoogle Scholar
  26. 26.
    Garnavi R, Aldeen M, Celebi ME, Varigos G, Finch S (2011) Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Comput Med Imaging Graph 35(2):105–115 CrossRefGoogle Scholar
  27. 27.
    Gerbert B, Maurer T, Berger T, Pantilat S, McPhee SJ, Wolff M, Bronstone A, Caspers N (1996) Primary care physicians as gatekeepers in managed care: primary care physicians’ and dermatologists’ skills at secondary prevention of skin cancer. Arch Dermatol 132(9):1030–1038 CrossRefGoogle Scholar
  28. 28.
    Gordon AD (1987) A review of hierarchical classification. J R Stat Soc A 150(2):119–137 zbMATHCrossRefGoogle Scholar
  29. 29.
    Green A, Martin N, McKenzie G, Pfitzner J, Quintarelli F, Thomas BW, O’Rourke M, Knight N (1991) Computer image analysis of pigmented skin lesions. Melanoma Res 1:231–236 CrossRefGoogle Scholar
  30. 30.
    Haralick RM, Shanmungam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621 CrossRefGoogle Scholar
  31. 31.
    Hintz-madsen M, Hansen LK, Larsen J, Olesen E, Drzewiecki KT (1995) Design and evaluation of neural classifiers application to skin lesion classification. In: Proceedings of the 1995 IEEE workshop on neural networks for signal processing V, pp 484–493 CrossRefGoogle Scholar
  32. 32.
    Iyatomi H, Celebi ME, Schaefer G, Tanaka M (2011) Automated color calibration method for dermoscopy images. Comput Med Imaging Graph 35(2):89–98 CrossRefGoogle Scholar
  33. 33.
    Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37 CrossRefGoogle Scholar
  34. 34.
    Jain AK, Zongker D (1997) Feature-selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158 CrossRefGoogle Scholar
  35. 35.
    Ko CB, Walton S, Keczkes K, Bury HPR, Nicholson C (1994) The emerging epidemic of skin cancer. Br J Dermatol 130:269–272 CrossRefGoogle Scholar
  36. 36.
    Laskaris N, Ballerini L, Fisher RB, Aldridge B, Rees J (2010) Fuzzy description of skin lesions. In: Manning DJ, Abbey CK (eds) Medical imaging 2010: image perception, observer performance, and technology assessment. Proceedings of the SPIE, vol 7627, pp 762,717-1–762,717-10 Google Scholar
  37. 37.
    Lee TK, Claridge E (2005) Predictive power of irregular border shapes for malignant melanomas. Skin Res Technol 11(1):1–8 CrossRefGoogle Scholar
  38. 38.
    Lehmann TM, Palm C (2001) Color line search for illuminant estimation in real-world scenes. J Opt Soc Am A 18(11):2679–2691 CrossRefGoogle Scholar
  39. 39.
    Li X, Aldridge B, Ballerini L, Fisher R, Rees J (2009) Depth data improves skin lesion segmentation. In: Proceedings of the 12th international conference on medical image computing and computer assisted intervention (MICCAI), London, pp 1100–1107 Google Scholar
  40. 40.
    Maglogiannis I, Doukas CN (2009) Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inf Technol Biomed 13(5):721–733 CrossRefGoogle Scholar
  41. 41.
    Maglogiannis I, Pavlopoulos S, Koutsouris D (2005) An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images. IEEE Trans Inf Technol Biomed 9(1):86–98 CrossRefGoogle Scholar
  42. 42.
    Martínez-Otzeta JM, Sierra B, Lazkano E, Astigarraga A (2006) Classifier hierarchy learning by means of genetic algorithms. Pattern Recognit Lett 27(16):1998–2004 CrossRefGoogle Scholar
  43. 43.
    Mete M, Kockara S, Aydin K (2011) Fast density-based lesion detection in dermoscopy images. Comput Med Imaging Graph 35(2):128–136 CrossRefGoogle Scholar
  44. 44.
    Morrison A, O’Loughlin S, Powell FC (2001) Suspected skin malignancy: a comparison of diagnoses of family practitioners and dermatologists in 493 patients. Int J Dermatol 40(2):104–107 CrossRefGoogle Scholar
  45. 45.
    Murtagh F (1983) A survey of recent advances in hierarchical clustering algorithms. Comput J 26(4):354–359 zbMATHGoogle Scholar
  46. 46.
    Ohta YI, Kanade T, Sakai T (1980) Color information for region segmentation. Comput Graph Image Process 13(1):222–241 CrossRefGoogle Scholar
  47. 47.
    Pourghassem H, Ghassemian H (2008) Content-based medical image classification using a new hierarchical merging scheme. Comput Med Imaging Graph 32(8):651–661 CrossRefGoogle Scholar
  48. 48.
    Rahman MM, Desai BC, Bhattacharya P (2006) Image retrieval-based decision support system for dermatoscopic images. In: IEEE symposium on computer-based medical systems. IEEE Computer Society, Los Alamitos, pp 285–290 CrossRefGoogle Scholar
  49. 49.
    Rigel DS, Russak J, Friedman R (2010) The evolution of melanoma diagnosis: 25 years beyond the ABCDs. CA: Cancer J Clinicians 60(5):301–316 CrossRefGoogle Scholar
  50. 50.
    Rodriguez C, Boto F, Soraluze I, Pérez A (2002) An incremental and hierarchical K-NN classifier for handwritten characters. In: Proceedings of the 16th international conference on pattern recognition (ICPR’02), vol 3. IEEE Computer Society, Washington, pp 98–101 Google Scholar
  51. 51.
    Rosado B, Menzies S, Harbauer A, Pehamberger H, Wolff K, Binder M, Kittler H (2003) Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis. Arch Dermatol 139(3):361–367 CrossRefGoogle Scholar
  52. 52.
    Sadeghi M, Razmara M, Lee TK, Atkins M (2011) A novel method for detection of pigment network in dermoscopic images using graphs. Comput Med Imaging Graph 35(2):137–143 CrossRefGoogle Scholar
  53. 53.
    Salah B, Alshraideh M, Beidas R, Hayajneh F (2011) Skin cancer recognition by using a neuro-fuzzy system. Cancer Inform 10:1–11 Google Scholar
  54. 54.
    Schaefer G, Rajab MI, Celebi ME, Iyatomi H (2011) Colour and contrast enhancement for improved skin lesion segmentation. Comput Med Imaging Graph 35(2):99–104 CrossRefGoogle Scholar
  55. 55.
    Schmid-Saugeons P, Guillod J, Thiran JP (2003) Towards a computer-aided diagnosis system for pigmented skin lesions. Comput Med Imaging Graph 27:65–78 CrossRefGoogle Scholar
  56. 56.
    Seidenari S, Pellacani G, Pepe P (1998) Digital videomicroscopy improves diagnostic accuracy for melanoma. J Am Acad Dermatol 39(2):175–181 CrossRefGoogle Scholar
  57. 57.
    Stoecker WV, Wronkiewiecz M, Chowdhury R, Stanley RJ, Xu J, Bangert A, Shrestha B, Calcara DA, Rabinovitz HS, Oliviero M, Ahmed F, Perry LA, Drugge R (2011) Detection of granularity in dermoscopy images of malignant melanoma using color and texture features. Comput Med Imaging Graph 35(2):144–147 CrossRefGoogle Scholar
  58. 58.
    Sun A, Lim EP, Ng WK (2003) Performance measurement framework for hierarchical text classification. J Am Soc Inf Sci Technol 54:1014–1028 CrossRefGoogle Scholar
  59. 59.
    Tommasi T, Dedelaers T (2010) In: The medical image classification task. ImageCLEF: the information retrieval series, vol 32, pp 221–238 Google Scholar
  60. 60.
    Viola KV, Tolpinrud WL, Gross CP, Kirsner RS, Imaeda S, Federman DG (2011) Outcomes of referral to dermatology for suspicious lesions: implications for teledermatology. Arch Dermatol 147(5):556–560 CrossRefGoogle Scholar
  61. 61.
    Wang H, Moss RH, Chen X, Stanley RJ, Stoecker WV, Celebi ME, Malters JM, Grichnik JM, Marghoob AA, Rabinovitz HS, Menzies SW, Szalapski TM (2011) Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images. Comput Med Imaging Graph 35(2):116–120 CrossRefGoogle Scholar
  62. 62.
    Wettschereck D, Aha DW, Mohri T (1997) A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif Intell Rev 11:273–314 CrossRefGoogle Scholar
  63. 63.
    Wollina U, Burroni M, Torricelli R, Gilardi S, Dell’Eva G, Helm C, Bardey W (2007) Digital dermoscopy in clinical practise: a three-centre analysis. Skin Res Technol 13:133–142 CrossRefGoogle Scholar
  64. 64.
    Zanotto M (2010) Visual description of skin lesions. Master’s thesis, School of Informatics, University of Edinburgh Google Scholar
  65. 65.
    Zanotto M, Ballerini L, Aldridge B, Fisher RB, Rees J (2011) Visual cues do not improve skin lesion ABC(D) grading. In: Manning DJ, Abbey CK (eds) Medical imaging 2011: image perception, observer performance, and technology assessment. Proceedings of the SPIE, vol 7966, pp 79,660U-1–79,660U-10 Google Scholar
  66. 66.
    Zhou H, Schaefer G, Celebi ME, Lin F, Liu T (2011) Gradient vector flow with mean shift for skin lesion segmentation. Comput Med Imaging Graph 35(2):121–127 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Lucia Ballerini
    • 1
  • Robert B. Fisher
    • 1
  • Ben Aldridge
    • 2
  • Jonathan Rees
    • 2
  1. 1.School of InformaticsUniversity of EdinburghEdinburghUK
  2. 2.Department of DermatologyUniversity of EdinburghEdinburghUK

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