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2013 | OriginalPaper | Buchkapitel

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

verfasst von : Lucia Ballerini, Robert B. Fisher, Ben Aldridge, Jonathan Rees

Erschienen in: Color Medical Image Analysis

Verlag: Springer Netherlands

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Abstract

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).

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Vorheriges Kapitel Color and Spatial Features Integrated Normalized Distance for Density Based Border Detection in Dermoscopy Images

Nächstes Kapitel Color Quantization of Dermoscopy Images Using the K-Means Clustering Algorithm

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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 CrossRef

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 CrossRef

Aldridge RB, Li X, Ballerini L, Fisher RB, Rees JL (2010) Teaching dermatology using 3-dimensional virtual reality. Archives of Dermatology 149(10)

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

Armengol E (2011) Classification of melanomas in situ using knowledge discovery with explained case-based reasoning. Artif Intell Med 51:93–105 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

12.

Cavalcanti PG, Scharcanski J (2011) Automated prescreening of pigmented skin lesions using standard cameras. Comput Med Imaging Graph 35(6):481–491 CrossRef

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

14.

Celebi ME, Iyatomi H, Schaefer G, Stoecker WV (2009) Lesion border detection in dermoscopy images. Comput Med Imaging Graph 33(2):148–153 CrossRef

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 CrossRef

16.

Celebi ME, Stoecker WV, Moss RH (2011) Advances in skin cancer image analysis. Comput Med Imaging Graph 35(2):83–84 CrossRef

17.

Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27 MATHCrossRef

18.

Cancer research UK (CRUK). CancerStats, Internet (2011). URL http://info.cancerresearchuk.org/cancerstats. Accessed 03/08/2011

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 CrossRef

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

21.

Day GR, Barbour RH (2000) Automated melanoma diagnosis: where are we at? Skin Res Technol 6:1–5 CrossRef

22.

Dimitrovski I, Kocev D, Loskovska S, Dzeroski S (2011) Hierarchical annotation of medical images. Pattern Recognit 44(10–11):2436–2449 CrossRef

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

24.

Duwairi R, Al-Zubaidi R (2011) A hierarchical K-NN classifier for textual data. Int Arab J Inf Technol 8(3):251–259

25.

Fix E, Hodges JL (1989) Discriminatory analysis. Nonparametric discrimination: consistency properties. Int Stat Rev 57(3):238–247 MATHCrossRef

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 CrossRef

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 CrossRef

28.

Gordon AD (1987) A review of hierarchical classification. J R Stat Soc A 150(2):119–137 MATHCrossRef

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 CrossRef

30.

Haralick RM, Shanmungam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621 CrossRef

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 CrossRef

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 CrossRef

33.

Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37 CrossRef

34.

Jain AK, Zongker D (1997) Feature-selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158 CrossRef

35.

Ko CB, Walton S, Keczkes K, Bury HPR, Nicholson C (1994) The emerging epidemic of skin cancer. Br J Dermatol 130:269–272 CrossRef

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

37.

Lee TK, Claridge E (2005) Predictive power of irregular border shapes for malignant melanomas. Skin Res Technol 11(1):1–8 CrossRef

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 CrossRef

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

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 CrossRef

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 CrossRef

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 CrossRef

43.

Mete M, Kockara S, Aydin K (2011) Fast density-based lesion detection in dermoscopy images. Comput Med Imaging Graph 35(2):128–136 CrossRef

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 CrossRef

45.

Murtagh F (1983) A survey of recent advances in hierarchical clustering algorithms. Comput J 26(4):354–359 MATH

46.

Ohta YI, Kanade T, Sakai T (1980) Color information for region segmentation. Comput Graph Image Process 13(1):222–241 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

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

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 CrossRef

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 CrossRef

53.

Salah B, Alshraideh M, Beidas R, Hayajneh F (2011) Skin cancer recognition by using a neuro-fuzzy system. Cancer Inform 10:1–11

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 CrossRef

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 CrossRef

56.

Seidenari S, Pellacani G, Pepe P (1998) Digital videomicroscopy improves diagnostic accuracy for melanoma. J Am Acad Dermatol 39(2):175–181 CrossRef

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 CrossRef

58.

Sun A, Lim EP, Ng WK (2003) Performance measurement framework for hierarchical text classification. J Am Soc Inf Sci Technol 54:1014–1028 CrossRef

59.

Tommasi T, Dedelaers T (2010) In: The medical image classification task. ImageCLEF: the information retrieval series, vol 32, pp 221–238

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 CrossRef

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 CrossRef

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 CrossRef

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 CrossRef

64.

Zanotto M (2010) Visual description of skin lesions. Master’s thesis, School of Informatics, University of Edinburgh

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

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 CrossRef

Titel: A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions
verfasst von: Lucia Ballerini
Robert B. Fisher
Ben Aldridge
Jonathan Rees
Verlag: Springer Netherlands
Buch: Color Medical Image Analysis
Print ISBN: 978-94-007-5388-4

Electronic ISBN: 978-94-007-5389-1

Copyright-Jahr: 2013
DOI: https://doi.org/10.1007/978-94-007-5389-1_4

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