Journal of Medical Systems

, 39:177 | Cite as

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices

  • Mercedes Filho
  • Zhen Ma
  • João Manuel R. S. TavaresEmail author
Mobile Systems
Part of the following topical collections:
  1. Mobile Systems


In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.


Skin lesion Dermoscopy Quantification Classification Mobile application 



This work is funded by European Regional Development Funds (ERDF), through the Operational Programme ‘Thematic Factors of Competitiveness’ (COMPETE), and Portuguese Funds, through the Fundação para a Ciência e a Tecnologia (FCT), under the project: FCOMP-01-0124-FEDER-028160/PTDC/BBB- BMD/3088/2012. The second author also thanks FCT for the post-doc grant: SFRH/BPD/97844/2013.


  1. 1.
    Cakir, B. O., Adamson, P., and Cingi, C., Epidemiology and economica burden of nonmelonoma skin cancer. Facial Plast. Surg. Clin. North Am. 20:419–422, 2012.CrossRefPubMedGoogle Scholar
  2. 2.
    Dubas, L. E., and Ingraffea, A., Nonmelanoma skin cancer. Facial Plast. Surg. Clin. North Am. 21:43–53, 2013.CrossRefPubMedGoogle Scholar
  3. 3.
    World Cancer Report, World Health Organization, Chapter 5.14, ISBN 9283204298, 2014.Google Scholar
  4. 4.
    Lozano, R., et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 380:2095–2128, 2011.CrossRefGoogle Scholar
  5. 5.
    Wang, S. W., et al., Current technologies for the in vivo diagnosis of cutaneous melanomas. Clin. Dermatol. 22(3):217–222, 2004.CrossRefPubMedGoogle Scholar
  6. 6.
    Ruocco, E., et al., Noninvasive imaging of skin tumors. Dermatol. Surg. 30:301–310, 2004.PubMedGoogle Scholar
  7. 7.
    Smith, L., and MacNeil, S., State of the art in non-invasive imaging of cutaneous melanoma. Skin Res. Technol. 17(3):257–269, 2011.CrossRefPubMedGoogle Scholar
  8. 8.
    Lorentzen, H., Weismann, K., Petersen, C. S., Larsen, F. G., Secher, L., and Skodt, V., Clinical and dermoscopic diagnosis of malignant melanoma. Assessed by expert and non-expert groups. Acta Derm. Venereol. 79(4):301–304, 1999.CrossRefPubMedGoogle Scholar
  9. 9.
    Ascierto, P. A., et al., Sensitivity and specificity of epiluminiscence miscroscopy: Evaluation on a sample of 2731 excised cutaneous pigmented lesions. Br. J. Dermatol. 142:893–898, 2000.CrossRefPubMedGoogle Scholar
  10. 10.
    Vestergaard, M. E., Macaskill, P., Holt, P. E., and Menzies, S. W., Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: A meta-analysis of studies performed in a clinical setting. Br. J. Dermatol. 159:669–676, 2008.PubMedGoogle Scholar
  11. 11.
    Zortea, M., Schopf, T. R., Thon, K., Geilhufe, M., Hindberg, K., Kirchesch, H., Mollerson, K., Schulz, J., Skrovseth, S. O., and Godtliebsen, F., 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. 60:13–26, 2014.CrossRefPubMedGoogle Scholar
  12. 12.
    Skvara, H., Burnett, P., Jones, J., Duschek, N., Plassmann, P., and Thirion, J. P., Quantification of skin lesions with a 3D stereovision camera system: Validation and clinical applications. Skin Res. Technol. 19:182–190, 2013.CrossRefGoogle Scholar
  13. 13.
    Zouridakis, G., Wadhawan, T., Situ, N., Hu, R., Yuan, X., Lancaster, K., and Queen, C. M., Melanoma and other skin lesion detection using smart hand-held devices. Methods Mol. Biol. 1256:459–496, 2015.CrossRefPubMedGoogle Scholar
  14. 14.
    Wadhawan, T., Situ, N., Lancaster, K., Yuan, X. and Zouridakis, G., SkinScan©: A portable library for melanoma detection on Hand-Held devices. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 133–136, 2011.Google Scholar
  15. 15.
    Ramlakhan, K., and Shang, Y., A mobile automated skin lesion classification system. 23rd IEEE International Conference on Tools with Artificial Intelligence, 138–141, 2011.Google Scholar
  16. 16.
    Karargyris, A., Karargyris, O., and Pantelopoulos, A., DERMA/care: An advanced image-processing mobile application for monitoring skin cancer. IEEE 24th International Conference on Tools with Artificial Intelligence, 1–7, 2012.Google Scholar
  17. 17.
    Doukas, C., Stagkopoulos, P., Kiranoudis, C., and Maglogiannis, I., Automated skin lesion assessment using mobile technologies and cloud platforms. IEEE Annual Conference, Engineering in Medicine and Biology Society, 2444–2447, 2013.Google Scholar
  18. 18.
    Maier, T., Kulichova, D., Schotten, K., Astrid, R., Ruzicka, T., Berking, C., and Udrea, A., Accuracy of a smartphone application using fractal image analysis of pigmented moles compared to clinical diagnosis and histological result. J. Eur. Acad. Dermatol. Venereol. 29(4):663–667, 2015.CrossRefPubMedGoogle Scholar
  19. 19.
    Bankman, I. N. (editor), Handbook of medical imaging: Processing and analysis, Academic Press Series, 910 pp., 2000.Google Scholar
  20. 20.
    Gonzalez, R. C., and Woods, R. E., Digital image processing, 2nd edition. Prentice Hall, New Jersey, p. 190, 2002.Google Scholar
  21. 21.
    Perona, P., and Malik, J., Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7):629–639, 1990.CrossRefGoogle Scholar
  22. 22.
    Sonka, M, Hlavac, V., and Boyle, R., Image processing, analysis, and machine vision, 2nd. ed., PWS, 800 pp., 1998.Google Scholar
  23. 23.
    Tomasi, C., and Manduchi, R., Bilateral filtering for gray and color images. IEEE Int. Conf. Comput. Vis. 839–846, 1998.Google Scholar
  24. 24.
    Butt, I., and Rajpoot, N., Multilateral filtering: A novel framework for generic similarity-based image denoising. IEEE Int. Conf. Image Process. 2981–2984, 2009.Google Scholar
  25. 25.
    Zhang, M., Bilateral filter in image processing. Master’s Thesis, Louisiana State University, Baton Rouge, LA, 2009.Google Scholar
  26. 26.
    Al-Abayechi, A. A. A., Logeswaran, R., Xiaoning Guo, and Wooi-Haw Tan, Lesion border detection in dermoscopy images using bilateral filter. IEEE Int. Conf. Signal Image Process. Appl. 365–368, 2013.Google Scholar
  27. 27.
    Silveira, M., et al., Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J. Sel. Top. Sign. Proces. 3(1):35–45, 2009.CrossRefGoogle Scholar
  28. 28.
    Uemura, T., Koutaki, G., and Uchimura, K., Image segmentation based on edge detection using boundary code. Int. J. Innov. Comput. Inf. Control 7(10):11, 2011.Google Scholar
  29. 29.
    Canny, J., A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6):679–698, 1986.CrossRefPubMedGoogle Scholar
  30. 30.
    Yasmin, J. H. J., Sathik, M. M., and Beevi, S. Z., Effective border detection of noisy real skin lesions for skin lesion diagnosis by robust segmentation algorithm. Int. J. Adv. Res. Comput. Sci. 1(3):110–115, 2010.Google Scholar
  31. 31.
    Yasmin, J. H. J., and Sadiq, M. M., An improved iterative segmentation algorithm using Canny edge detector with iterative median filter for skin lesion border detection. Int. J. Comput. Appl. 50(6):37–42, 2012.Google Scholar
  32. 32.
    Al-Amri, S. S., Kalyankar, N. V., and Khamitkar, S. D., Image segmentation by using threshold techniques. J. Comput. 2(5):1–4, 2010.Google Scholar
  33. 33.
    Abbas, A. A., Guo, X., Tan, W. H., and Jalab, H. A., Combined spline and B-spline for an improved automatic skin lesion segmentation in dermoscopic images using optimal color channel systems-level quality improvement. J. Med. Syst. 28:1–8, 2014.Google Scholar
  34. 34.
    Otsu, N., A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern. 9(1):62–66, 1979.CrossRefGoogle Scholar
  35. 35.
    Garnavi, R., Aldeen, M., Celebi, M. E., Varigos, G., and Finch, S., Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Comput. Med. Imaging Graph. 35(2):105–115, 2011.CrossRefPubMedGoogle Scholar
  36. 36.
    Gould, S., Gao, T., and Koller, D., Region-based segmentation and object detection. Adv. Neural Inf. Process. Syst. 655–663, 2009.Google Scholar
  37. 37.
    Mumford, D., and Shah, J., Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5):577–685, 1989.CrossRefGoogle Scholar
  38. 38.
    Chan, T. F., and Vese, L. A., Active contours without edges. IEEE Trans. Image Process. 10(2):266–277, 2001.CrossRefPubMedGoogle Scholar
  39. 39.
    Capdehourat, G., Corez, A., Bazzano, A., Alondo, R., and Musé, P., Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions. Pattern Recogn. Lett. 32(16):2187–2196, 2011.CrossRefGoogle Scholar
  40. 40.
    Oliveira, R. B., Tavares, J. M. R. S., Marranghello, N., and Pereira, A. S., An approach to edge detection in images of skin lesions by chan-vese model. 8th Doctoral Symposium in Informatics Engineering, Oporto, 1, 2013.Google Scholar
  41. 41.
    Rastgarpour, M., and Shanbehzadeh, J., The status quo of artificial intelligence methods in automatic medical image segmentation. Int. J. Comput. Theory Eng. 5(1):4, 2013.Google Scholar
  42. 42.
    Haykin, S. S., Neural networks: A comprehensive foundation. Prentice Hall, New Jersey, p. 842, 1999.Google Scholar
  43. 43.
    Haupt, R. L., and Haupt, S. E., Practical genetic algorithms, 2nd edition. John Wiley & Sons, New Jersey, p. 253, 2004.Google Scholar
  44. 44.
    Aswin, R. B. Hybrid genetic algorithm - artificial neural network classifier for skin cancer detection. International Conference on Control, Instrumentation, Communication and Computational Technologies, 1304–1309, 2014.Google Scholar
  45. 45.
    Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active contour models. Int. J. Comput. Vis. 1(4):321–331, 1988.CrossRefGoogle Scholar
  46. 46.
    Xu, C., and Prince, J. L., Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3):359–369, 1998.CrossRefPubMedGoogle Scholar
  47. 47.
    Zhou, H., Schaefer, G., Celebi, M., Iyatomi, H., Norton, K. A., Liu, T., and Lin, F., Skin lesion segmentation using an improved snake model. IEEE Annual International Conference on Engineering in Medicine and Biology Society, 1974–1977, 2010.Google Scholar
  48. 48.
    Osher, S., and Sethian, J. A., Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations. J. Comput. Phys. 79(1):12–49, 1988.CrossRefGoogle Scholar
  49. 49.
    Ma, Z., and Tavares, J. M. R. S., Segmentation of skin lesions using level set method. Computational modeling of objects presented in images: Fundamentals, methods, and applications, 228–233, 2014Google Scholar
  50. 50.
    Maeda, J., Kawano, A., Sato, S., and Suzuki, Y., Number-driven perceptual segmentation of natural color images for easy decision of optimal result. IEEE Int. Conf. Image Proces. 2:265–268, 2007.Google Scholar
  51. 51.
    Maeda, J., Kawano, A., Sato, S., and Suzuki, Y., Unsupervised perceptual segmentation of natural color images using fuzzy-based hierarchical algorithm. Image Anal. Lect. Notes Comput. Sci. 4522:462–471, 2007. Springer.CrossRefGoogle Scholar
  52. 52.
    Maeda, J., Kawano, A., Yamauchi, S., Suzuki, Y., Marçal, A. R. S., and Mendonça, T., Perceptual image segmentation using fuzzy-based hierarchical algorithm and its application to dermoscopy images. IEEE Conference on Soft Computing in Industrial Applications, 66–71, 2008.Google Scholar
  53. 53.
    Rahman, M. M., Bhattacharya, P., and Desai, B. C., A multiple expert-based melanoma recognition system for dermoscopic images of pigmented skin lesions. 8th IEEE International Conference on BioInformatics and BioEngineering, 1–6, 2008.Google Scholar
  54. 54.
    Castiello, C., Catellano, G., and Fanelli, A. M., Neuro-fuzzy analysis of dermatological images. IEEE Int. Joint Conf. Neural Netw. 4:3247–3252, 2004.Google Scholar
  55. 55.
    Mendel, H. M., and John, R. I. B., Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2):117–127, 2002.CrossRefGoogle Scholar
  56. 56.
    Cover, T., and Hart, P., Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1):21–27, 1967.CrossRefGoogle Scholar
  57. 57.
    Ballerini, L., Fisher, R. B., Aldridge, B., and Rees, J., A color and texture based hierarchical k-NN approach to the classification of non-melanoma skin lesions. Lect. Notes Comput. Vis. Biomech. 6:63–86, 2013.CrossRefGoogle Scholar
  58. 58.
    John, J. M., Samual, S. S., and John, N. M., Segmentation of skin lesions from digital images using texture distinctiveness with neural network. Int. J. Adv. Res. Comput. Commun. Eng. 3(8):7777–7780, 2014.Google Scholar
  59. 59.
    Lloyd, S. P., Least squares quantization is PCM. IEEE Trans. Inf. Theory 28(2):129–137, 1982.CrossRefGoogle Scholar
  60. 60.
    Ma, Z., and Tavares, J. M. R. S., A novel approach to segment skin lesions in dermoscopic images based on a deformable model. IEEE J. Biomed. Health Inf. 2015. doi: 10.1109/JBHI.2015.2390032.Google Scholar
  61. 61.
    Sirakov, N. M., Ou, Y. -L., and Mete, M., Skin lesion feature vectors classification in models of a Riemannian manifold. Ann. Math. Artif. Intell., 2–15, 2014Google Scholar
  62. 62.
    Hunter, R. S., Photoelectric color-difference meter. J. Opt. Soc. Am. 38(7):661, 1948.Google Scholar
  63. 63.
    Gevers, T., van der Weijer, J., and Stokman, H., Color feature detection. In: Lukac, R., and Plataniotis, K. N. (Eds.) Color Image Processing: Emerging Applications, Chapter 1., CRC Press, 1–27, 2006.Google Scholar
  64. 64.
    White, R., Rigel, D. S., and Friedman, R., Computer applications in the diagnosis and prognosis of malignant melanoma. Dermatol. Clin. 9:695–702, 1992.Google Scholar
  65. 65.
    Hu, M. - K., Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory. 179–187, 1967.Google Scholar
  66. 66.
    Mertzios, B. G., and Tsirikolias, K., Statistical shape discrimination and clustering using an efficient set of moments. Pattern Recogn. Lett. 14:517–522, 1993.CrossRefGoogle Scholar
  67. 67.
    Gutkowicz-Krushin, D., Elbaum, M., Szwaykowski, P., and Kopf, A. W., Can early malignant melanoma be differentiated from atypical melanocytic nevus by invivo techniques? Skin Res. Technol. 3:15–22, 1997.CrossRefGoogle Scholar
  68. 68.
    Mallat, S., A theory of multi-resolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11:674–693, 1989.CrossRefGoogle Scholar
  69. 69.
    Gopinath, R. A., and Burrus, C. S., Wavelet transforms and filter banks. In: Chui, C. K. (Ed.), Wavelets – A Tutorial in Theory and Applications. Academic, San Diego, pp. 603–654, 1992.Google Scholar
  70. 70.
    Easton Jr., R. L., Fourier methods in imaging. Wiley, 954 pp., 2010.Google Scholar
  71. 71.
    Kim, S. D., Lee, J. H., and Kim, J. K., A new chain-coding algorithm for binary images using run-length codes. Comput. Vis. Graphics Image Process. 41:114–128, 1988.CrossRefGoogle Scholar
  72. 72.
    Davidson, J., Thinning and skeletonizing: a tutorial and overview. In: Dougherty, E. (Ed.), Digital Image Processing: Fundamental and Applications. Marcel Dekker, New York, 1991.Google Scholar
  73. 73.
    Lam, L., Lee, S., and Suen, C., Thinning methodologies—A comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14:868–885, 1992.CrossRefGoogle Scholar
  74. 74.
    Zhang, T. Y., and Suen, C. Y., A fast parallel algorithm for thinning digital patterns. Commun. Assoc. Comput. Mach. 27(3):236–239, 1984.Google Scholar
  75. 75.
    Tuceryan, M., Moment based texture segmentation. Pattern Recogn. Lett. 15:659–668, 1994.CrossRefGoogle Scholar
  76. 76.
    Haralick, R. M., Shanmugam, K., and Dinstein, I., Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3:610–621, 1973.CrossRefGoogle Scholar
  77. 77.
    Handels, H., Ross, T., Kreusch, J., Wolff, H. H., and Poppl, S. J., Computer-supported diagnosis of melanoma in profilometry. Methods Inf. Med. 38:43–49, 1999.PubMedGoogle Scholar
  78. 78.
    Shanmugavadivu, P., and Sivakumar, V., Fractal dimension based texture analysis of digital images. Procedia Eng. Int. Conf. Model. Optim. Comput. 38:2981–2986, 2012.Google Scholar
  79. 79.
    Barnsley, M., Fractals everywhere. Academic, Toronto, 1988.Google Scholar
  80. 80.
    Lundhal, T., Ohley, W. J., Kay, S. M., and Siffert, R., Fractional Brownian motion: A maximum likelihood estimator and its applications to image texture. IEEE Trans. Med. Imaging 5:152–161, 1989.CrossRefGoogle Scholar
  81. 81.
    Penn, A. I., and Loew, M. H., Estimating fractal dimension with fractal interpolation function models. IEEE Trans. Med. Imaging 16:930–937, 1997.CrossRefPubMedGoogle Scholar
  82. 82.
    Nailon, W. H., Texture analysis methods for medical image characterisation. In: Youxin Mao (Ed.), Biomedical Imaging, 27 pp., 2010.Google Scholar
  83. 83.
    Clawson, K. M., et al., Determination of optimal axes for skin lesion asymmetry quantification. IEEE Int. Conf. Image Proces. 2:453–456, 2007.Google Scholar
  84. 84.
    Tosca, A., et al., Development of a three-dimensional surface imaging system for melanocytic skin lesion evaluation. J. Biomed. Opt. 18(1):13, 2013.CrossRefGoogle Scholar
  85. 85.
    Delibasis, K., Undrill, P. E., and Cameron, G. G., Designing Fourier descriptor based geometric models for object interpretation in medical images using genetic algorithms. Comput. Vis. Image Underst. 66:286–300, 1997.CrossRefGoogle Scholar
  86. 86.
    Naf, M., Szekely, G., Kikinis, R., Shenton, M. E., and Kubler, O., 3D Vornoi skeletons and their usage for the characterization and recognition of 3D organ shape. Comput. Vis. Image Underst. 66:147–161, 1997.CrossRefGoogle Scholar
  87. 87.
    Palagyi, K., and Kuba, A., A hybrid thinning algorithm for 3D medical images. J. Comput. Inf. Technol. 6:149–164, 1998.Google Scholar
  88. 88.
    Zhou, Y., and Toga, A. W., Efficient skeletonization of volumetric objects. IEEE Trans. Vis. Comput. Graph. 5:196–209, 1999.PubMedCentralCrossRefPubMedGoogle Scholar
  89. 89.
    Pehamberger, H., Steiner, A., and Wolff, K., In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions. J. Am. Acad. Dermotol. 17(4):571–583, 1987.CrossRefGoogle Scholar
  90. 90.
    Friedman, R. J., Rigel, D. S., and Kopf, A. W., Early detection of malignant melanoma: The role of physician examination and self-examination of the skin. Cancer J. Clin. 35(3):130–151, 1985.CrossRefGoogle Scholar
  91. 91.
    Henning, J. S., The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy. J. Am. Acad. Dermatol. 56:45–52, 2007.CrossRefPubMedGoogle Scholar
  92. 92.
    Henning, J. S., Stein, J. A., Yeung, J., and Dusza, J. W., CASH algorithm for dermoscopy revisited. Arch. Dermatol. 144:554–555, 2008.CrossRefPubMedGoogle Scholar
  93. 93.
    Johr, R. H., Dermoscopy: Alternative melanocytic algorithms - the ABCD rule of dermatoscopy, menzies scoring method, and 7-point check-list. Clin. Dermatol. 20:240–247, 2002.CrossRefPubMedGoogle Scholar
  94. 94.
    Menzies, S. W., Ingvar, C., Crotty, K. A., and McCarthy, W. H., Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. Arch. Dermatol. 132(10):1178–1182, 1996.CrossRefPubMedGoogle Scholar
  95. 95.
    Argenziano, G., Fabbrocini, G., Carli, P., and De Giorgi, V., Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Arch. Dermatol. 134:1563–1570, 1998.CrossRefPubMedGoogle Scholar
  96. 96.
    Shimizu, K., Iyatomi, H., Celebi, M. E., Norton, K. A., and Tanaka, M., Four-class classification of skin lesions with task decomposition strategy. IEEE Trans. Biomed. Eng. 62:274–283, 2015.CrossRefPubMedGoogle Scholar
  97. 97.
    Schaefer, G., Krawczyk, B., Celebi, M. E., and Iyatomi, H., An ensemble classification approach for melanoma diagnosis. Memet. Comput. 6(4):223–240, 2014.CrossRefGoogle Scholar
  98. 98.
    Schaefer, G., Krawczyk, B., Celebi, M. E., Iyatomi, H., and Hassanien, A. E., Melanoma classification based on ensemble classification of dermoscopy image features. Commun. Comput. Inf. Sci. 488:291–298, 2014.CrossRefGoogle Scholar
  99. 99.
    Masood, A., Al-Jumaily, A., and Anam, K., Texture analysis based automated decision support system for classification of skin cancer using SA-SVM. Lect. Notes Comput. Sci 8835:101–109, 2014. Springer.CrossRefGoogle Scholar
  100. 100.
    Vasconcelos, M. J. M., Rosado, L., and Ferreira, M., Principal axes-based asymmetry assessment methodology for skin lesion image analysis. Lect. Notes Comput. Sci 8888:21–31, 2014. Springer.CrossRefGoogle Scholar
  101. 101.
    Celebi, M. E., and Zomberg, A., Automated quantification of clinically significant colors in dermoscopy images and its application to skin lesion classification. IEEE Syst. J. 8:980–984, 2014.CrossRefGoogle Scholar
  102. 102.
    Takuri, M., Al-Jumaily, A., and Mahmoud, M. K. A., Automatic recognition of melanoma using support vector machines: A study based on Wavelet, Curvelet and color features. Proceedings of the International Conference on Industrial Automation, Information and Communications Technology, 70–75, 2014.Google Scholar
  103. 103.
    Dhinagar, N. J., and Celenk, M., Performance assessment of the use of the RGB and LAB color spaces for non-invasive skin cancer classification. 29th International Conference on Computers and Their Applications, 243–248, 2014.Google Scholar
  104. 104.
    Barata, C., Ruela, M., Francisco, M., Mendonça, T., and Marques, J. S., Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst. J. 8(3):965–979, 2014.CrossRefGoogle Scholar
  105. 105.
    Rameshkumar, P., Santhi, B., and Monisha, M., Significance of color & texture features in computerized melanoma diagnosis using soft computing techniques. Int. J. Appl. Eng. Res. 9(12):1875–1884, 2014.Google Scholar
  106. 106.
    Masood, A., Al-Jumaily, A., and Aung, Y. M., Scaled conjugate gradient based decision support system for automated diagnosis of skin cancer. Proceedings of the IASTED International Conference on Biomedical Engineering, 196–203, 2014.Google Scholar
  107. 107.
    Masood, A., Al-Jumaily, A., and Adnan, T., Development of automated diagnostic system for skin cancer: Performance analysis of neural network learning algorithms for classification. Lect. Notes Comput. Sci 8681:837–844, 2014.CrossRefGoogle Scholar
  108. 108.
    Wolf, J. A., Moreau, J. F., Akilov, O., Patton, T., English, J. C., III, Ho, J., and Ferris, L. K., Diagnostic inaccuracy of smartphone applications for melanoma detection. J. Am. Med. Assoc. Dermatol. 149(4):422–426, 2013.Google Scholar
  109. 109.
    Abuzaghleh, O., Faezipour, M., and Barkana, B. D., Skincure: An innovative smart phone-based application to assist in melanoma early detection and prevention. Signal Image Process. Int. J. 5(6):15, 2014. doi: 10.5121/sipij.2014.5601.Google Scholar
  110. 110.
    Massone, C., Brunasso, A. M., Campbell, T. M., and Soyer, H. P., Mobile teledermoscopy-melanoma diagnosis by one click?, Semin. Cutan. Med. Surg. 203–205, 2009.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de EngenhariaUniversidade do PortoPortoPortugal

Personalised recommendations