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

Journal of Medical Systems

Volumn 39, Issue 11, 2015, Pages

  Filho, Mercedes ^a   Ma, Zhen ^a   Tavares, João Manuel R S ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

Classification; Dermoscopy; Mobile application; Quantification; Skin lesion

Indexed keywords

ALGORITHM; AUTOMATION; DISEASE CLASSIFICATION; GENERAL DEVICE; HUMAN; MICROCOMPUTER; MOBILE APPLICATION; QUANTITATIVE ANALYSIS; REVIEW; SKIN CANCER; SKIN DEFECT; SKIN PIGMENTATION; COMPUTER ASSISTED DIAGNOSIS; DEVICES; EPILUMINESCENCE MICROSCOPY; PATHOLOGY; PROCEDURES; SKIN NEOPLASMS;

ALGORITHMS; DERMOSCOPY; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; SKIN NEOPLASMS;

EID: 84942805971     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-015-0354-8     Document Type: Review

References (110)

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.
2. Dubas, L. E., and Ingraffea, A., Nonmelanoma skin cancer. Facial Plast. Surg. Clin. North Am. 21:43–53, 2013.
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.
5. Wang, S. W., et al., Current technologies for the in vivo diagnosis of cutaneous melanomas. Clin. Dermatol. 22(3):217–222, 2004.
6. Ruocco, E., et al., Noninvasive imaging of skin tumors. Dermatol. Surg. 30:301–310, 2004.
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.
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.
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.
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.
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.
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.
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.
14. ©: 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.
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.
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.
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.
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.
29. Canny, J., A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6):679–698, 1986.
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.
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.
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.
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.
34. Otsu, N., A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern. 9(1):62–66, 1979.
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.
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.
38. Chan, T. F., and Vese, L. A., Active contours without edges. IEEE Trans. Image Process. 10(2):266–277, 2001.
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.
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.
42. Haykin, S. S., Neural networks: A comprehensive foundation. Prentice Hall, New Jersey, p. 842, 1999.
43. Haupt, R. L., and Haupt, S. E., Practical genetic algorithms, 2nd edition. John Wiley & Sons, New Jersey, p. 253, 2004.
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.
46. Xu, C., and Prince, J. L., Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3):359–369, 1998.
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.
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.
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.
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.
55. Mendel, H. M., and John, R. I. B., Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2):117–127, 2002.
56. Cover, T., and Hart, P., Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1):21–27, 1967.
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.
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.
59. Lloyd, S. P., Least squares quantization is PCM. IEEE Trans. Inf. Theory 28(2):129–137, 1982.
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.
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.
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.
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.
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.
68. Mallat, S., A theory of multi-resolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11:674–693, 1989.
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.
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.
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.
73. Lam, L., Lee, S., and Suen, C., Thinning methodologies—A comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14:868–885, 1992.
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.
75. Tuceryan, M., Moment based texture segmentation. Pattern Recogn. Lett. 15:659–668, 1994.
76. Haralick, R. M., Shanmugam, K., and Dinstein, I., Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3:610–621, 1973.
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.
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.
79. Barnsley, M., Fractals everywhere. Academic, Toronto, 1988.
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.
81. Penn, A. I., and Loew, M. H., Estimating fractal dimension with fractal interpolation function models. IEEE Trans. Med. Imaging 16:930–937, 1997.
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.
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.
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.
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.
87. Palagyi, K., and Kuba, A., A hybrid thinning algorithm for 3D medical images. J. Comput. Inf. Technol. 6:149–164, 1998.
88. Zhou, Y., and Toga, A. W., Efficient skeletonization of volumetric objects. IEEE Trans. Vis. Comput. Graph. 5:196–209, 1999.
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.
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.
91. Henning, J. S., The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy. J. Am. Acad. Dermatol. 56:45–52, 2007.
92. Henning, J. S., Stein, J. A., Yeung, J., and Dusza, J. W., CASH algorithm for dermoscopy revisited. Arch. Dermatol. 144:554–555, 2008.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.