A. C. Society, Cancer facts & figures 2014, 2014.

A. Forsea, V. Del-marmol, E. De-vries, E. Bailey, and A. Geller, Melanoma incidence and mortality in Europe: new estimates, persistent disparities, British Journal of Dermatology, vol.46, issue.5, pp.1124-1130, 2012.
DOI : 10.1111/j.1365-2133.2012.11125.x

N. R. Abbasi and H. M. Shaw, Early Diagnosis of Cutaneous Melanoma, JAMA, vol.292, issue.22, pp.2771-2776, 2004.
DOI : 10.1001/jama.292.22.2771

D. Manning, A. Gale, and E. Krupinski, Perception research in medical imaging, The British Journal of Radiology, vol.78, issue.932, 2014.
DOI : 10.1259/bjr/72087985

M. Rastgoo, R. Garcia, O. Morel, and F. Marzani, Automatic differentiation of melanoma from dysplastic nevi, Computerized Medical Imaging and Graphics, vol.43, pp.44-52, 2015.
DOI : 10.1016/j.compmedimag.2015.02.011
URL : https://hal.archives-ouvertes.fr/hal-01457799

J. S. Duncan and N. Ayache, Medical image analysis: Progress over two decades and the challenges ahead Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.22, issue.1, pp.85-106, 2000.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

K. Korotkov and R. 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

T. Mendonça, P. M. Ferreira, J. S. Marques, A. R. Marcal, and J. Rozeira, Ph 2-a dermoscopic image database for research and benchmarking, Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp.5437-5440, 2013.

C. Barata, M. Ruela, M. Francisco, T. Mendona, and J. Marques, Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features, IEEE Systems Journal, vol.8, issue.3, pp.965-979, 2014.
DOI : 10.1109/JSYST.2013.2271540

C. Barata, J. S. Marques, and J. Rozeira, The Role of Keypoint Sampling on the Classification of Melanomas in Dermoscopy Images Using Bag-of-Features, Pattern Recognition and Image Analysis, pp.715-723, 2013.
DOI : 10.1007/978-3-642-38628-2_85

M. Ruela, C. Barata, T. Mendonca, and J. S. Marques, On the role of shape in the detection of melanomas, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), pp.268-273, 2013.
DOI : 10.1109/ISPA.2013.6703751

M. Ruela, C. Barata, and J. S. Marques, What Is the Role of Color Symmetry in the Detection of Melanomas?, Advances in Visual Computing, pp.1-10, 2013.
DOI : 10.1007/978-3-642-41914-0_1

O. Abuzaghleh, B. D. Barkana, and M. Faezipour, Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention, IEEE Long Island Systems, Applications and Technology (LISAT) Conference 2014, pp.1-6, 2014.
DOI : 10.1109/LISAT.2014.6845199

M. Rastgoo, O. Morel, F. Marzani, and R. Garcia, Ensemble approach for differentiation of malignant melanoma, The International Conference on Quality Control by Artificial Vision 2015, pp.953415-953415, 2015.

C. Barata, J. S. Marques, and M. E. Celebi, Towards an automatic bag-of-features model for the classification of dermoscopy images: The influence of segmentation, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), pp.274-279, 2013.
DOI : 10.1109/ISPA.2013.6703752

D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.1150-1157, 1999.
DOI : 10.1109/ICCV.1999.790410

A. Vedaldi and B. Fulkerson, Vlfeat, Proceedings of the international conference on Multimedia, MM '10, pp.1469-1472, 2010.
DOI : 10.1145/1873951.1874249

J. Van-de-weijer and C. Schmid, Coloring Local Feature Extraction, Computer Vision?ECCV 2006, pp.334-348, 2006.
DOI : 10.1002/col.10049
URL : https://hal.archives-ouvertes.fr/inria-00548576

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, Robust face recognition via sparse representation Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.31, issue.2, pp.210-227, 2009.

M. Elad and M. Aharon, Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries, IEEE Transactions on Image Processing, vol.15, issue.12, pp.3736-3745, 2006.
DOI : 10.1109/TIP.2006.881969

D. Sidibé, I. Sadek, and F. Mériaudeau, Discrimination of retinal images containing bright lesions using sparse coded features and SVM, Computers in biology and medicine 62, pp.175-184, 2015.
DOI : 10.1016/j.compbiomed.2015.04.026

R. Rubinstein, M. Zibulevsky, and M. Elad, Efficient implementation of the k-svd algorithm using batch orthogonal matching pursuit, CS Technion, vol.40, issue.8, pp.1-15, 2008.

M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, 2010.
DOI : 10.1007/978-1-4419-7011-4

S. G. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

Y. C. Pati, R. Rezaiifar, and P. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp.40-44, 1993.
DOI : 10.1109/ACSSC.1993.342465

G. Davis, S. Mallat, and M. Avellaneda, Adaptive greedy approximations, Constructive Approximation, vol.21, issue.1, pp.57-98, 1997.
DOI : 10.1007/BF02678430