Skip to main content

Advertisement

SpringerLink
Log in

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

  • Mobile Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. 1http://www.skincancer.org/skin-cancer-information/skin-cancer-facts

  2. 2http://www.cancerresearchuk.org/cancer-info/cancerstats/types/skin

  3. 3http://www.cancer.net/cancer-types/skin-cancer-non-melanoma/statistics

  4. 4http://apps.nccd.cdc.gov/uscs

  5. 5http://www.anthem.com/medicalpolicies/policies/mp_pw_a049923.htm

References

  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.

    Article  PubMed  Google Scholar 

  2. Dubas, L. E., and Ingraffea, A., Nonmelanoma skin cancer. Facial Plast. Surg. Clin. North Am. 21:43–53, 2013.

    Article  PubMed  Google Scholar 

  3. World Cancer Report, World Health Organization, Chapter 5.14, ISBN 9283204298, 2014.

  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.

    Article  Google Scholar 

  5. Wang, S. W., et al., Current technologies for the in vivo diagnosis of cutaneous melanomas. Clin. Dermatol. 22(3):217–222, 2004.

    Article  PubMed  Google Scholar 

  6. Ruocco, E., et al., Noninvasive imaging of skin tumors. Dermatol. Surg. 30:301–310, 2004.

    PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

  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.

  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.

  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.

  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.

    Article  CAS  PubMed  Google Scholar 

  19. Bankman, I. N. (editor), Handbook of medical imaging: Processing and analysis, Academic Press Series, 910 pp., 2000.

  20. Gonzalez, R. C., and Woods, R. E., Digital image processing, 2nd edition. Prentice Hall, New Jersey, p. 190, 2002.

    Google Scholar 

  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.

    Article  Google Scholar 

  22. Sonka, M, Hlavac, V., and Boyle, R., Image processing, analysis, and machine vision, 2nd. ed., PWS, 800 pp., 1998.

  23. Tomasi, C., and Manduchi, R., Bilateral filtering for gray and color images. IEEE Int. Conf. Comput. Vis. 839–846, 1998.

  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.

  25. Zhang, M., Bilateral filter in image processing. Master’s Thesis, Louisiana State University, Baton Rouge, LA, 2009.

  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.

  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.

    Article  Google Scholar 

  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. Canny, J., A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6):679–698, 1986.

    Article  CAS  PubMed  Google Scholar 

  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. 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. 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. 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. Otsu, N., A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern. 9(1):62–66, 1979.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  36. Gould, S., Gao, T., and Koller, D., Region-based segmentation and object detection. Adv. Neural Inf. Process. Syst. 655–663, 2009.

  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.

    Article  Google Scholar 

  38. Chan, T. F., and Vese, L. A., Active contours without edges. IEEE Trans. Image Process. 10(2):266–277, 2001.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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. Haykin, S. S., Neural networks: A comprehensive foundation. Prentice Hall, New Jersey, p. 842, 1999.

    Google Scholar 

  43. Haupt, R. L., and Haupt, S. E., Practical genetic algorithms, 2nd edition. John Wiley & Sons, New Jersey, p. 253, 2004.

    Google Scholar 

  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.

  45. Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active contour models. Int. J. Comput. Vis. 1(4):321–331, 1988.

    Article  Google Scholar 

  46. Xu, C., and Prince, J. L., Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3):359–369, 1998.

    Article  CAS  PubMed  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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, 2014

  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. 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.

    Article  Google Scholar 

  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.

  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.

  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. Mendel, H. M., and John, R. I. B., Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2):117–127, 2002.

    Article  Google Scholar 

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

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Lloyd, S. P., Least squares quantization is PCM. IEEE Trans. Inf. Theory 28(2):129–137, 1982.

    Article  Google Scholar 

  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. 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, 2014

  62. Hunter, R. S., Photoelectric color-difference meter. J. Opt. Soc. Am. 38(7):661, 1948.

    Google Scholar 

  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.

  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. Hu, M. - K., Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory. 179–187, 1967.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  68. Mallat, S., A theory of multi-resolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11:674–693, 1989.

    Article  Google Scholar 

  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. Easton Jr., R. L., Fourier methods in imaging. Wiley, 954 pp., 2010.

  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.

    Article  Google Scholar 

  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. Lam, L., Lee, S., and Suen, C., Thinning methodologies—A comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14:868–885, 1992.

    Article  Google Scholar 

  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. Tuceryan, M., Moment based texture segmentation. Pattern Recogn. Lett. 15:659–668, 1994.

    Article  Google Scholar 

  76. Haralick, R. M., Shanmugam, K., and Dinstein, I., Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3:610–621, 1973.

    Article  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  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. Barnsley, M., Fractals everywhere. Academic, Toronto, 1988.

    Google Scholar 

  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.

    Article  Google Scholar 

  81. Penn, A. I., and Loew, M. H., Estimating fractal dimension with fractal interpolation function models. IEEE Trans. Med. Imaging 16:930–937, 1997.

    Article  CAS  PubMed  Google Scholar 

  82. Nailon, W. H., Texture analysis methods for medical image characterisation. In: Youxin Mao (Ed.), Biomedical Imaging, 27 pp., 2010.

  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. Tosca, A., et al., Development of a three-dimensional surface imaging system for melanocytic skin lesion evaluation. J. Biomed. Opt. 18(1):13, 2013.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Zhou, Y., and Toga, A. W., Efficient skeletonization of volumetric objects. IEEE Trans. Vis. Comput. Graph. 5:196–209, 1999.

    Article  PubMed Central  PubMed  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  91. Henning, J. S., The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy. J. Am. Acad. Dermatol. 56:45–52, 2007.

    Article  PubMed  Google Scholar 

  92. Henning, J. S., Stein, J. A., Yeung, J., and Dusza, J. W., CASH algorithm for dermoscopy revisited. Arch. Dermatol. 144:554–555, 2008.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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.

    Article  Google Scholar 

  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. 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.

  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.

    Article  Google Scholar 

  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. 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. 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.

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

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

    Mercedes Filho, Zhen Ma & João Manuel R. S. Tavares

Authors
  1. Mercedes Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. João Manuel R. S. Tavares
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to João Manuel R. S. Tavares.

Additional information

This article is part of the Topical Collection on Mobile Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Filho, M., Ma, Z. & Tavares, J.M.R.S. A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices. J Med Syst 39, 177 (2015). https://doi.org/10.1007/s10916-015-0354-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0354-8

Keywords

Search

Navigation