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Computational diagnosis of skin lesions from dermoscopic images using combined features

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Abstract

There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is to find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour- and texture-related features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step, and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimum-path forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm.

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References

  1. Scharcanski J, Celebi ME (2013) Computer vision techniques for the diagnosis of skin cancer. Springer, Berlin

    Google Scholar 

  2. Iyatomi H, Oka H, Celebi ME, Hashimoto M, Hagiwara M, Tanaka M, Ogawa K (2008) An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Comput Med Imaging Graph 32(7):566–579. https://doi.org/10.1016/j.compmedimag.2008.06.005

    Article  Google Scholar 

  3. Barata C, Celebi ME, Marques JS (2017) Development of a clinically oriented system for melanoma diagnosis. Pattern Recogn 69:270–285. https://doi.org/10.1016/j.patcog.2017.04.023

    Article  Google Scholar 

  4. Johr RH (2002) Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist. Clin Dermatol 20(3):240–247. https://doi.org/10.1016/S0738-081X(02)00236-5

    Article  Google Scholar 

  5. Maglogiannis I, Doukas CN (2009) Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inf Technol Biomed 13(5):721–733. https://doi.org/10.1109/titb.2009.2017529

    Article  Google Scholar 

  6. 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. https://doi.org/10.1016/j.compmedimag.2007.01.003

    Article  Google Scholar 

  7. Garnavi R, Aldeen M, Bailey J (2012) Computer-aided diagnosis of melanoma using border- and wavelet-based texture analysis. IEEE Trans Inf Technol Biomed 16(6):1239–1252. https://doi.org/10.1109/titb.2012.2212282

    Article  Google Scholar 

  8. Celebi ME, Zornberg A (2014) Automated quantification of clinically significant colors in dermoscopy images and its application to skin lesion classification. IEEE Syst J 8(3):980–984. https://doi.org/10.1109/JSYST.2014.2313671

    Article  Google Scholar 

  9. Shimizu K, Iyatomi H, Celebi ME, Norton K-A, Tanaka M (2015) Four-class classification of skin lesions with task decomposition strategy. IEEE Trans Biomed Eng 62(1):274–283. https://doi.org/10.1109/TBME.2014.2348323

    Article  Google Scholar 

  10. Barata C, Celebi ME, Marques JS, Rozeira J (2016) Clinically inspired analysis of dermoscopy images using a generative model. Comput Vis Image Underst 151:124–137. https://doi.org/10.1016/j.cviu.2015.09.011

    Article  Google Scholar 

  11. Sadri AR, Azarianpour S, Zekri M, Celebi ME, Sadri S (2017) WN-based approach to melanoma diagnosis from dermoscopy images. IET Image Proc 11(7):475–482. https://doi.org/10.1049/iet-ipr.2016.0681

    Article  Google Scholar 

  12. Barata C, Ruela M, Francisco M, Mendonça T, Marques JS (2013) Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst J 8(3):965–979. https://doi.org/10.1109/JSYST.2013.2271540

    Article  Google Scholar 

  13. Materka A, Strzelecki M (1998) Texture analysis methods: a review. COST B11 report. Technical University of Lodz, Brussels

    Google Scholar 

  14. Iyatomi H, Norton K, Celebi ME, Schaefer G, Tanaka M, Ogawa K (2010) Classification of melanocytic skin lesions from non-melanocytic lesions. In: Annual international conference of the IEEE engineering in medicine and biology society buenos aires, Aug 31–Sept 4, 2010. IEEE, pp 5407–5410. https://doi.org/10.1109/iembs.2010.5626500

  15. Celebi ME, Iyatomi H, Stoecker WV, Moss RH, Rabinovitz HS, Argenziano G, Soyer HP (2008) Automatic detection of blue-white veil and related structures in dermoscopy images. Comput Med Imaging Graph 32(8):670–677. https://doi.org/10.1016/j.compmedimag.2008.08.003

    Article  Google Scholar 

  16. Oliveira RB, Marranghello N, Pereira AS, Tavares JMRS (2016) A computational approach for detecting pigmented skin lesions in macroscopic images. Expert Syst Appl 61:53–63. https://doi.org/10.1016/j.eswa.2016.05.017

    Article  Google Scholar 

  17. Leo GD, Paolillo A, Sommella P, Fabbrocini G (2010) Automatic diagnosis of melanoma: a software system based on the 7-point check-list. In: 43rd international conference on system sciences, Hawaii Jan 5–8, 2010. IEEE, pp 1–10. https://doi.org/10.1109/hicss.2010.76

  18. Yuan X, Yang Z, Zouridakis G, Mullani N (2006) SVM-based texture classification and application to early melanoma detection. In: 28th annual international conference of the IEEE engineering in medicine and biology society, New York, Aug 30–Sept 3, 2006. IEEE, pp 4775–4778. https://doi.org/10.1109/iembs.2006.260056

  19. Webb AR (2003) Statistical pattern recognition, 2nd edn. Wiley, England

    MATH  Google Scholar 

  20. Japkowicz N, Shah M (2011) Evaluating learning algorithms: a classification perspective. Cambridge University Press, Cambridge

    Book  Google Scholar 

  21. Rahman MM, Bhattacharya P, Desai BC (2008) A multiple expert-based melanoma recognition system for dermoscopic images of pigmented skin lesions. In: 8th IEEE international conference on international conference on bioinformatics and bioengineering, Athens, October 8–10, 2008. IEEE, pp 1–6. https://doi.org/10.1109/bibe.2008.4696799

  22. Papa JP, Falcao AX, Suzuki CT (2009) Supervised pattern classification based on optimum-path forest. Int J Imaging Syst Technol 19(2):120–131. https://doi.org/10.1002/ima.20188

    Article  Google Scholar 

  23. Guyon I, Gunn S, Nikravesh M, Zadeh L (2006) Feature extraction: foundations and applications, vol 207. Studies in fuzziness and soft computing. Springer, Berlin. https://doi.org/10.1007/978-3-540-35488-8

    Book  MATH  Google Scholar 

  24. Oliveira RB, Papa JP, Pereira AS, Tavares JMRS (2016) Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput Appl 27:1–24. https://doi.org/10.1007/s00521-016-2482-6

    Article  Google Scholar 

  25. Abbas Q, Celebi ME, Garcia IF, Ahmad W (2013) Melanoma recognition framework based on expert definition of ABCD for dermoscopic images. Skin Res Technol 19(1):e93–e102. https://doi.org/10.1111/j.1600-0846.2012.00614.x

    Article  Google Scholar 

  26. Costa LdF, Cesar Junior RM (2009) Shape classification and analysis: theory and practice, 2nd edn. CRC Press, Boca Raton

    Book  Google Scholar 

  27. Clawson KM, Morrow P, Scotney B, McKenna J, Dolan O (2009) Analysis of pigmented skin lesion border irregularity using the harmonic wavelet transform. In: 13th international machine vision and image processing conference Dublin, Sept 2–4, 2009. IEEE, pp 18–23

  28. Zhou Y, Smith M, Smith L, Warr R (2010) A new method describing border irregularity of pigmented lesions. Skin Res Technol 16:66–76. https://doi.org/10.1111/j.1600-0846.2009.00403.x

    Article  Google Scholar 

  29. Lee TK, McLean DI, Atkins MS (2003) Irregularity index: a new border irregularity measure for cutaneous melanocytic lesions. Med Image Anal 7(1):47–64. https://doi.org/10.1016/S1361-8415(02)00090-7

    Article  Google Scholar 

  30. Celebi ME, Iyatomi H, Schaefer G, Stoecker WV (2009) Lesion border detection in dermoscopy images. Comput Med Imaging Graph 33(2):148–153. https://doi.org/10.1016/j.compmedimag.2008.11.002

    Article  Google Scholar 

  31. Lissner I, Urban P (2012) Toward a unified color space for perception-based image processing. IEEE Trans Image Process 21(3):1153–1168. https://doi.org/10.1109/TIP.2011.2163522

    Article  MathSciNet  MATH  Google Scholar 

  32. Tkalcic M, Tasic JF (2003) Colour spaces: perceptual, historical and applicational background. In: Proceedings in the IEEE region 8 EUROCON 2003: computer as a tool Ljubljana, Sept 22–24, 2003. IEEE, pp 304–308. https://doi.org/10.1109/eurcon.2003.1248032

  33. Silva CS, Marcal AR (2013) Colour-based dermoscopy classification of cutaneous lesions: an alternative approach. Comput Methods Biomech Biomed Eng Imag Vis 1(4):211–224. https://doi.org/10.1080/21681163.2013.803683

    Article  Google Scholar 

  34. Al-Akaidi M (2004) Fractal speech processing. Cambridge University Press, New York

    Book  Google Scholar 

  35. Scheunders P, Livens S, Van de Wouwer G, Vautrot P, Van Dyck D (1998) Wavelet-based texture analysis. Int J Comput Sci Inf Manag 1(2):22–34

    Google Scholar 

  36. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621. https://doi.org/10.1109/TSMC.1973.4309314

    Article  Google Scholar 

  37. Mallat SG (1987) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693. https://doi.org/10.1109/34.192463

    Article  MATH  Google Scholar 

  38. Abedini M, Chen Q, Codella NCF, Garnavi R, Sun X (2015) Accurate and scalable system for automatic detection of malignant melanoma. In: Celebi ME, Mendonca T, Marques JS (eds) Dermoscopy image analysis. CRC Press, Boca Raton, pp 293–343. https://doi.org/10.1201/b19107-11

    Chapter  Google Scholar 

  39. Witten IH, Frank E, Hall MA (2016) Data mining: practical machine learning tools and techniques, 4th edn. Morgan Kaufmann, San Francisco

    Google Scholar 

  40. Chawla NV (2005) Data mining for imbalanced datasets: an overview. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, New York, pp 853–867. https://doi.org/10.1007/0-387-25465-X_40

    Chapter  Google Scholar 

  41. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  42. Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(3):131–156. https://doi.org/10.1016/S1088-467X(97)00008-5

    Article  Google Scholar 

  43. Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502. https://doi.org/10.1109/TKDE.2005.66

    Article  Google Scholar 

  44. Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In: Bergadano F, De Raedt L (eds) Machine learning: ECML-94, vol 784. Lecture notes in computer science. Springer, Berlin, pp 171–182. https://doi.org/10.1007/3-540-57868-4_57

    Chapter  Google Scholar 

  45. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27. https://doi.org/10.1109/tit.1967.1053964

    Article  MATH  Google Scholar 

  46. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(1):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

    Article  MathSciNet  MATH  Google Scholar 

  47. Hall MA (2000) Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the 17th international conference on machine learning, San Francisco, June 29–July 02, 2000. Morgan Kaufmann, 657793, pp 359–366

  48. Hand D, Mannila H, Smyth P (2001) Principles of data mining. The MIT Press, London

    Google Scholar 

  49. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference on artificial intelligence, Quebec, Aug 20–25, 1995. Morgan Kaufmann, pp 1137–1145

  50. Congdon P (2007) Bayesian statistical modelling, vol 704, 2nd edn. Wiley, Chichester

    MATH  Google Scholar 

  51. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco

    Google Scholar 

  52. Haykin SS (1999) Neural networks: a comprehensive foundation. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  53. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167. https://doi.org/10.1023/A:1009715923555

    Article  Google Scholar 

  54. Han J, Kamber M (2006) Data mining: concepts and techniques. Elsevier, San Francisco

    MATH  Google Scholar 

  55. Platt JC (1999) Fast training of support vector machines using sequential minimal optimization. Advances in Kernel methods. MIT Press Cambridge, USA, pp 185–208

    Google Scholar 

  56. Amorim WP, Falcão AX, de Carvalho MH (2014) Semi-supervised pattern classification using optimum-path forest. In: 27th SIBGRAPI conference on graphics, patterns and images, Rio de Janeiro, Aug 26–30, 2014. IEEE, pp 111–118. https://doi.org/10.1109/sibgrapi.2014.45

  57. Gutman D, Codella NCF, Celebi E, Helba B, Marchetti M, Mishra N, Halpern AC (2016) Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC), arXiv preprint arXiv:1605.01397

  58. Arroyo JLG, Zapirain BG (2014) Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis. Comput Biol Med 44:144–157. https://doi.org/10.1016/j.compbiomed.2013.11.002

    Article  Google Scholar 

  59. Maglogiannis I, Delibasis KK (2015) Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy. Comput Methods Programs Biomed 118(2):124–133. https://doi.org/10.1016/j.cmpb.2014.12.001

    Article  Google Scholar 

  60. Zortea M, Schopf TR, Thon K, Geilhufe M, Hindberg K, Kirchesch H, Møllersen K, Schulz J, Skrøvseth SO, Godtliebsen F (2014) Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists. Artif Intell Med 60(1):13–26. https://doi.org/10.1016/j.artmed.2013.11.006

    Article  Google Scholar 

  61. Ma Z, Tavares JMRS (2016) A novel approach to segment skin lesions in dermoscopic images based on a deformable model. IEEE J Biomed Health Inf 20(2):615–623. https://doi.org/10.1109/JBHI.2015.2390032

    Article  Google Scholar 

  62. Lequan Y, Chen H, Dou Q, Qin J, Heng PA (2016) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2016.2642839

    Article  Google Scholar 

  63. Toussaint GT (1983) Solving geometric problems with the rotating calipers. In: Proceedings of IEEE Melecon, Athens, 1983, pp 1–8

  64. Yu-Len H, Ruey-Feng C (1999) Texture features for DCT-coded image retrieval and classification. In: IEEE international conference on acoustics, speech, and signal processing, Phoenix, Mar 15–19, 1999. IEEE, pp 3013–3016. https://doi.org/10.1109/icassp.1999.757475

  65. Chang T, Kuo CCJ (1993) Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process 2(4):429–441. https://doi.org/10.1109/83.242353

    Article  Google Scholar 

  66. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357. https://doi.org/10.1613/jair.953

    Article  MATH  Google Scholar 

  67. Schaefer G, Krawczyk B, Celebi ME, Iyatomi H (2014) An ensemble classification approach for melanoma diagnosis. Memet Comput 6(4):233–240. https://doi.org/10.1007/s12293-014-0144-8

    Article  Google Scholar 

  68. Sharma K, Virmani J (2017) A decision support system for classification of normal and medical renal disease using ultrasound images: a decision support system for medical renal diseases. Int J Ambient Comput Intell 8(2):52–69. https://doi.org/10.4018/IJACI.2017040104

    Article  Google Scholar 

  69. Wang D, He T, Li Z, Cao L, Dey N, Ashour AS, Balas VE, McCauley P, Lin Y, Xu J (2016) Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system. Neural Comput Appl. https://doi.org/10.1007/s0052

    Article  Google Scholar 

  70. Azzabi O, Njima CB, Messaoud H (2017) New approach of diagnosis by timed automata. Int J Ambient Comput Intell 8(3):76–93. https://doi.org/10.4018/IJACI.2017070105

    Article  Google Scholar 

  71. Li Z, Shi K, Dey N, Ashour AS, Wang D, Balas VE, McCauley P, Shi F (2017) Rule-based back propagation neural networks for various precision rough set presented KANSEI knowledge prediction: a case study on shoe product form features extraction. Neural Comput Appl 28(3):613–630. https://doi.org/10.1007/s0052

    Article  Google Scholar 

  72. Ghosh A, Sarkar A, Ashour AS, Balas-Timar D, Dey N, Balas VE (2015) Grid color moment features in glaucoma classification. Int J Adv Comput Sci Appl 6(9):1–14. https://doi.org/10.14569/IJACSA.2015.060913

    Article  Google Scholar 

  73. Li Z, Dey N, Ashour AS, Cao L, Wang Y, Wang D, McCauley P, Balas VE, Shi K, Shi F (2017) Convolutional neural network based clustering and manifold learning method for diabetic plantar pressure imaging dataset. J Med Imaging Health Inf 7(3):639–652. https://doi.org/10.1166/jmihi.2017.2082

    Article  Google Scholar 

  74. Kuncheva LI (2014) Combining pattern classifiers: methods and algorithms, 2nd edn. Wiley, New Jersey

    MATH  Google Scholar 

  75. Bengio Y (2009) Learning deep architectures for AI. Foundations and trends®. Mach Learn 2(1):1–127. https://doi.org/10.1561/2200000006

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

The first author would like to thank CNPq (“Conselho Nacional de Desenvolvimento Científico e Tecnológico”), in Brazil, for her Ph.D. Grant. Authors gratefully acknowledge the funding of Project NORTE-01-0145-FEDER-000022—SciTech—Science and Technology for Competitive and Sustainable Industries, co-financed by “Programa Operacional Regional do Norte” (NORTE2020), through “Fundo Europeu de Desenvolvimento Regional” (FEDER).

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  1. Departamento de Engenharia Mecânica, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Roberta B. Oliveira & João Manuel R. S. Tavares

  2. Departamento de Ciências de Computação e Estatística, Instituto de Biociências, Letras e Ciências Exatas, Universidade Estadual Paulista, Rua Cristóvão Colombo, 2265, São José do Rio Preto, SP, 15054-000, Brazil

    Aledir S. Pereira

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  1. Roberta B. Oliveira
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Correspondence to João Manuel R. S. Tavares.

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Oliveira, R.B., Pereira, A.S. & Tavares, J.M.R.S. Computational diagnosis of skin lesions from dermoscopic images using combined features. Neural Comput & Applic 31, 6091–6111 (2019). https://doi.org/10.1007/s00521-018-3439-8

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