W. Nachbar, T. Stolz, A. B. Merkle, T. Cognetta, M. Vogt et al., The ABCD rule of dermatoscopy, Journal of the American Academy of Dermatology, vol.30, issue.4, pp.551-559, 1994.
DOI : 10.1016/S0190-9622(94)70061-3

H. Johr, Dermoscopy: alternative melanocytic algorithms???the ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist, Clinics in Dermatology, vol.20, issue.3, pp.240-247, 2002.
DOI : 10.1016/S0738-081X(02)00236-5

B. Aldridge, M. Zanotto, L. Ballerini, R. B. Fisher, and J. L. Rees, Novice Identification of Melanoma: Not Quite as Straightforward as the ABCDs, Acta Dermato Venereologica, vol.91, issue.2, pp.125-130, 2011.
DOI : 10.2340/00015555-1070

R. Mclaughlin, H. Rikers, and . Schmidt, Is analytic information processing a feature of expertise in medicine?, Advances in Health Sciences Education, vol.39, issue.1, pp.123-128, 2008.
DOI : 10.1007/s10459-007-9080-4

. Norman, Building on Experience ??? The Development of Clinical Reasoning, New England Journal of Medicine, vol.355, issue.21, pp.2251-2252, 2006.
DOI : 10.1056/NEJMe068134

R. Korotkov and . Garcia, Computerized analysis of pigmented skin lesions: A review, Artificial Intelligence in Medicine, vol.56, issue.2, pp.69-90, 2012.
DOI : 10.1016/j.artmed.2012.08.002

R. Tanaka, M. Yamada, K. Tanaka, M. Shimizu, H. Tanaka et al., A study on the image diagnosis of melanoma, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1597-1600, 2004.
DOI : 10.1109/IEMBS.2004.1403485

A. Tenenhaus, J. Nkengne, C. Horn, A. Serruys, B. Giron et al., Detection of melanoma from dermoscopic images of naevi acquired under uncontrolled conditions, Skin Research and Technology, vol.16, issue.1, pp.85-97, 2010.
DOI : 10.1111/j.1600-0846.2009.00385.x

URL : https://hal.archives-ouvertes.fr/hal-00446575

G. Isasi, B. G. Zapirain, and A. M. Zorrilla, Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms, Computers in Biology and Medicine, vol.41, issue.9, pp.742-755, 2011.
DOI : 10.1016/j.compbiomed.2011.06.010

J. Amelard, A. Glaister, D. Wong, and . Clausi, High-Level Intuitive Features (HLIFs) for Intuitive Skin Lesion Description, IEEE Transactions on Biomedical Engineering, vol.62, issue.3, pp.820-831, 2015.
DOI : 10.1109/TBME.2014.2365518

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

R. Rastgoo, O. Garcia, F. Morel, and . Marzani, Automatic differentiation of melanoma from dysplastic nevi, Computerized Medical Imaging and Graphics, vol.43, issue.0, pp.44-52, 2015.
DOI : 10.1016/j.compmedimag.2015.02.011

URL : https://hal.archives-ouvertes.fr/hal-01457799

S. O. Zortea, T. R. Skrvseth, H. M. Schopf, F. Kirchesch, and . Godtliebsen, Automatic Segmentation of Dermoscopic Images by Iterative Classification, International Journal of Biomedical Imaging, vol.15, issue.1, p.2011, 2011.
DOI : 10.1111/j.1600-0846.2010.00460.x

A. Glaister, D. Wong, and . Clausi, Segmentation of Skin Lesions From Digital Images Using Joint Statistical Texture Distinctiveness, IEEE Transactions on Biomedical Engineering, vol.61, issue.4, pp.1220-1230, 2014.
DOI : 10.1109/TBME.2013.2297622

H. Celebi, G. Iyatomi, W. V. Schaefer, and . Stoecker, Lesion border detection in dermoscopy images, Computerized Medical Imaging and Graphics, vol.33, issue.2, pp.148-153, 2009.
DOI : 10.1016/j.compmedimag.2008.11.002

R. H. Erkol, R. Moss, W. V. Stanley, E. Stoecker, and . Hvatum, Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes, Skin Research and Technology, vol.8, issue.1, pp.17-26, 2005.
DOI : 10.1109/34.244675

S. Wazaefi, B. Paris, and . Fertil, Contribution of a classifier of skin lesions to the dermatologist's decision, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), pp.207-211, 2012.
DOI : 10.1109/IPTA.2012.6469560

M. Ojala, D. Pietikäinen, and . Harwood, A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, vol.29, issue.1, pp.51-59, 1996.
DOI : 10.1016/0031-3203(95)00067-4

M. Ojala, T. Pietikäainen, and . Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.24, pp.971-987, 2002.

J. Debayle and . Pinoli, General Adaptive Neighborhood Image Processing:, Journal of Mathematical Imaging and Vision, vol.13, issue.6, pp.245-266, 2006.
DOI : 10.1007/s10851-006-7451-8

URL : https://hal.archives-ouvertes.fr/hal-00128123

J. Pinoli and J. Debayle, General Adaptive Neighborhood Mathematical Morphology, 2009 16th IEEE International Conference on Image Processing (ICIP), pp.2249-2252, 2009.
DOI : 10.1109/ICIP.2009.5413979

J. González-castro, J. Debayle, and . Pinoli, Color Adaptive Neighborhood Mathematical Morphology and its application to pixel-level classification, Pattern Recognition Letters, vol.47, pp.50-62, 2014.
DOI : 10.1016/j.patrec.2014.01.007

. Angulo, Morphological colour operators in totally ordered lattices based on distances: Application to image filtering, enhancement and analysis, Computer Vision and Image Understanding, vol.107, issue.1-2, pp.56-73, 2007.
DOI : 10.1016/j.cviu.2006.11.008

J. Debayle and . Pinoli, General Adaptive Neighborhood Image Processing, Journal of Mathematical Imaging and Vision, vol.13, issue.6, pp.267-284, 2006.
DOI : 10.1007/s10851-006-7452-7

URL : https://hal.archives-ouvertes.fr/hal-00128123

K. Michielsen and H. De-raedt, Integral-geometry morphological image analysis, Physics Reports, vol.347, issue.6, pp.461-538, 2001.
DOI : 10.1016/S0370-1573(00)00106-X

D. Mecke and . Stoyan, Statistical Physics and Spatial Statistics, 2000.
DOI : 10.1007/3-540-45043-2

J. Rivollier, J. Debayle, and . Pinoli, Integral geometry and general adaptive neighborhood for multiscale image analysis, International Journal of Signal and Image Processing, vol.1, issue.3, pp.141-150, 2010.

. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, vol.27, issue.8, pp.861-874, 2006.
DOI : 10.1016/j.patrec.2005.10.010

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

. Chawla, Data mining for imbalanced datasets: An overview, " in Data Mining and Knowledge Discovery Handbook, pp.853-867, 2005.

J. Rivollier, J. Debayle, and . Pinoli, Adaptive Shape Diagrams for Multiscale Morphometrical Image Analysis, Journal of Mathematical Imaging and Vision, vol.29, issue.2, pp.51-68, 2014.
DOI : 10.1007/s10851-013-0439-2

URL : https://hal.archives-ouvertes.fr/hal-01003567