, 15, where the standard view results is compared with the image original randomly acquired. The implementation of these two ideas shows an accuracy of 86, 45% compared to amelard et al. [105] where the authors used illumination, asymmetry and border irregularities and obtained only 81.26%. REFERENCES
Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions, Pattern Recognition Letters, vol.32, issue.16, pp.2187-2196, 2011. ,
DOI : 10.1016/j.patrec.2011.06.015
, Skin Cancer Fundation Skin cancer information [Online; accessed on 2016, 2016.
Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation, International Journal of Biomedical Imaging, vol.357, issue.1760, 2011. ,
DOI : 10.1016/j.compbiomed.2006.08.002
, [Online; accessed on 2015, Dermatology Information System, 2015.
[Online; accessed on 2015, 2015. ,
How common is skin cancer?, 2015. [Online ; accessed on 08, 2016. ,
, Collection état des lieux et des connaissances, ouvrage collectif édité par l'inca, 2013.
, Catalogue-des-publications/Les-cancers-en-France-Edition, 2013.
Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis, Computers in biology and medicine, vol.44, pp.144-157, 2014. ,
,
, Melanoma: differences between asian and caucasian patients, Ann Acad Med Singapore, vol.41, issue.1, pp.17-20, 2012.
The evolution of melanoma diagnosis: 25 years beyond the abcds, CA: a cancer journal for clinicians, vol.60, issue.5, pp.301-316, 2010. ,
, American cancer society available online, statistics 2013
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
Early detection of malignant melanoma: The role of physician examination and self-examination of the skin, CA: a cancer journal for clinicians, vol.35, issue.3, pp.130-151, 1985. ,
Abcd rule of dermatoscopy-a new practical method for early recognition of malignant-melanoma, European Journal of Dermatology, vol.4, issue.7, pp.521-527, 1994. ,
Strategies for early melanoma detection: Approaches to the patient with nevi, Journal of the American Academy of Dermatology, vol.60, issue.5, pp.719-735, 2009. ,
DOI : 10.1016/j.jaad.2008.10.065
Overview of Advanced Computer Vision Systems for Skin Lesions Characterization, IEEE Transactions on Information Technology in Biomedicine, vol.13, issue.5, pp.721-733, 2009. ,
DOI : 10.1109/TITB.2009.2017529
Epiluminescence Microscopy for the Diagnosis of Doubtful Melanocytic Skin Lesions, Archives of Dermatology, vol.134, issue.12, pp.1563-1570, 1998. ,
DOI : 10.1001/archderm.134.12.1563
Seven-point checklist for melanoma, Clinical and Experimental Dermatology, vol.60, issue.2, pp.151-152, 1991. ,
DOI : 10.1038/bjc.1989.298
Computerized medical diagnosis of melanocytic lesions based on the abcd approach, CLEI Electronic Journal, vol.19, issue.2, pp.6-6, 2016. ,
Combination of features from skin pattern and ABCD analysis for lesion classification, Skin Research and Technology, vol.13, issue.1, pp.25-33, 2007. ,
DOI : 10.1016/B0-12-227240-4/00132-5
URL : http://collections.crest.ac.uk/200/1/fulltext.pdf
Seven-point checklist of dermoscopy revisited, British Journal of Dermatology, vol.62, issue.4, pp.785-790, 2011. ,
DOI : 10.1016/j.jaad.2009.08.049
Recognition of pigment network pattern in dermoscopy images based on fuzzy classification of pixels. Computer methods and programs in biomedicine, pp.61-69, 2018. ,
,
Vascular structures in skin tumors: a dermoscopy study, Archives of dermatology, vol.140, issue.12, pp.1485-1489, 2004. ,
Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics, pp.198-211, 2007. ,
A systematic review of computer-assisted diagnosis in diagnostic cancer imaging, European journal of radiology, vol.81, issue.1, pp.70-76, 2012. ,
Medical content-based retrieval for clinical decision support, 2012. ,
DOI : 10.1007/978-3-642-36678-9
Current status and future potential of computer-aided diagnosis in medical imaging. The British journal of radiology, 2014. ,
Automatic change detection in multiple pigmented skin lesions, 2014. ,
, [Online; accessed on 10, Cancer Resarch UK. Treating skin cancer, vol.14, 2016.
An overview of melanoma detection in dermoscopy images using image processing and machine learning, 2016. ,
Early diagnosis of cutaneous melanoma: revisiting the abcd criteria, Jama, issue.22, pp.2922771-2776, 2004. ,
Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms, International Journal of Biomedical Imaging, vol.9, issue.2, 2013. ,
DOI : 10.1097/00008390-199904000-00009
A state-of-the-art survey on lesion border detection in dermoscopy images, 2015. ,
Using the 7- point checklist as a diagnostic aid for pigmented skin lesions in general practice: a diagnostic validation study, Br J Gen Pract, issue.610, pp.63-345, 2013. ,
Comparative Performance of 4 Dermoscopic Algorithms by Nonexperts for the Diagnosis of Melanocytic Lesions, Archives of Dermatology, vol.141, issue.8, pp.1008-1014, 2005. ,
DOI : 10.1001/archderm.141.8.1008
Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology, British Journal of Dermatology, vol.3, issue.5, pp.981-984, 2003. ,
DOI : 10.1001/archderm.137.10.1343
Lesion border detection in dermoscopy images. Computerized medical imaging and graphics, pp.148-153, 2009. ,
Dullrazor R : A software approach to hair removal from images. Computers in biology and medicine, pp.533-543, 1997. ,
DOI : 10.1016/s0010-4825(97)00020-6
Dermascopic hair disocclusion using inpainting, Medical Imaging 2008: Image Processing, pp.691427-691427, 2008. ,
DOI : 10.1117/12.770776
URL : http://www.cs.sfu.ca/~stella/papers/2008/spie.pdf
E-shaver: An improved dullrazor R for ,
DOI : 10.1016/j.compbiomed.2011.01.003
, itally removing dark and light-colored hairs in dermoscopic images. Computers in biology and medicine, pp.139-145, 2011.
An effective hair removal algorithm for dermoscopy images, Skin Research and Technology, vol.19, issue.3, pp.230-235, 2013. ,
Segmentation of light and dark hair in dermoscopic images: a hybrid approach using a universal kernel, SPIE Medical Imaging. International Society for Optics and Photonics, pp.76234-76234, 2010. ,
PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma, Computerized Medical Imaging and Graphics, vol.33, issue.4, pp.275-282, 2009. ,
DOI : 10.1016/j.compmedimag.2009.01.003
VirtualShave: Automated hair removal from digital dermatoscopic images, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5145-5148, 2010. ,
DOI : 10.1109/IEMBS.2011.6091274
A robust hair segmentation and removal approach for clinical images of skin lesions, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.3315-3318, 2013. ,
DOI : 10.1109/EMBC.2013.6610250
Hair removal methods: A comparative study for dermoscopy images, Biomedical Signal Processing and Control, vol.6, issue.4, pp.395-404, 2011. ,
DOI : 10.1016/j.bspc.2011.01.003
Unsupervised skin lesions border detection via two-dimensional image analysis. Computer methods and programs in biomedicine, pp.1-15, 2011. ,
DOI : 10.1016/j.cmpb.2010.06.016
A Feature-Preserving Hair Removal Algorithm for Dermoscopy Images, Skin Research and Technology, vol.4, issue.1, pp.27-36, 2013. ,
DOI : 10.1109/TSMC.1974.5408463
A System for the Detection of Pigment Network in Dermoscopy Images Using Directional Filters, IEEE Transactions on Biomedical Engineering, vol.59, issue.10, pp.592744-2754, 2012. ,
DOI : 10.1109/TBME.2012.2209423
Independent Histogram Pursuit for Segmentation of Skin Lesions, IEEE Transactions on Biomedical Engineering, vol.55, issue.1, pp.157-161, 2008. ,
DOI : 10.1109/TBME.2007.910651
Contrast enhancement in dermoscopy images by maximizing a histogram bimodality measure, 16th IEEE International Conference on Image Processing (ICIP), pp.2601-2604, 2009. ,
Automated pre?processing method for dermoscopic images and its application to pigmented skin lesion segmentation, Color and Imaging Conference, pp.158-163, 2012. ,
Automated Digital Hair Removal by Threshold Decomposition and Morphological Analysis, International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, pp.15-26, 2015. ,
DOI : 10.1007/978-3-319-18720-4_2
Hair Enhancement in Dermoscopic Images Using Dual-Channel Quaternion Tubularness Filters and MRF-Based Multilabel Optimization, IEEE Transactions on Image Processing, vol.23, issue.12, pp.5486-5496, 2014. ,
DOI : 10.1109/TIP.2014.2362054
URL : http://www.cs.sfu.ca/%7Ehamarneh/ecopy/tip2014.pdf
Multiscale vessel enhancement filtering, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.130-137 ,
Capillary detection for clinical images of basal cell carcinoma, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.306-309, 2012. ,
DOI : 10.1109/ISBI.2012.6235545
An Image Inpainting Technique Based on the Fast Marching Method, Journal of Graphics Tools, vol.93, issue.4, pp.23-34, 2004. ,
DOI : 10.1073/pnas.93.4.1591
URL : https://pure.rug.nl/ws/files/14404904/2004JGraphToolsTelea.pdf
Predictive power of irregular border shapes for malignant melanomas, Skin Research and Technology, vol.11, issue.1, pp.1-8, 2005. ,
Localizing Region-Based Active Contours, IEEE Transactions on Image Processing, vol.17, issue.11, pp.2029-2039, 2008. ,
DOI : 10.1109/TIP.2008.2004611
URL : http://www.shawnlankton.com/wp-content/uploads/articles/lankton-lrbac-TIP-2008.pdf
Active contours without edges, IEEE Transactions on image processing, vol.10, issue.2, pp.266-277, 2001. ,
A multiphase level set framework for image segmentation using the mumford and shah model, International journal of computer vision, vol.50, issue.3, pp.271-293, 2002. ,
Optimal approximations by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, vol.3, issue.5, pp.577-685, 1989. ,
DOI : 10.1109/TPAMI.1984.4767596
URL : https://dash.harvard.edu/bitstream/handle/1/3637121/Mumford_OptimalApproxPiece.pdf?sequence=1
Computer???Aided Diagnosis of Pigmented Skin Dermoscopic Images, MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, pp.105-115, 2011. ,
DOI : 10.1017/CBO9780511801389
Multiphase Soft Segmentation with Total Variation and H 1 Regularization, Journal of Mathematical Imaging and Vision, vol.18, issue.3, pp.98-111, 2010. ,
DOI : 10.1007/s10851-010-0195-5
URL : https://link.springer.com/content/pdf/10.1007%2Fs10851-010-0195-5.pdf
Multiphase image segmentation via equally distanced multiple well potential, Journal of Visual Communication and Image Representation, vol.25, issue.6, pp.1446-1459, 2014. ,
DOI : 10.1016/j.jvcir.2014.04.008
Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images, IEEE Journal of Selected Topics in Signal Processing, vol.3, issue.1 ,
DOI : 10.1109/JSTSP.2008.2011119
, IEEE Journal of Selected Topics in Signal Processing, vol.3, issue.1, pp.35-45, 2009.
Segmentation of skin cancer images using an extension of Chan and Vese model, 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), pp.442-447, 2015. ,
DOI : 10.1109/ICITEED.2015.7408987
URL : https://hal.archives-ouvertes.fr/hal-01319727
Wavelet transform fuzzy algorithms for dermoscopic image segmentation. Computational and mathematical methods in medicine, 2012. ,
A theory for multiresolution signal decomposition: the wavelet representation, IEEE transactions on pattern analysis and machine intelligence, vol.11, issue.7, pp.674-693, 1989. ,
Multi-scale descriptors for contour irregularity of skin lesion using wavelet decomposition, 2010 3rd International Conference on Biomedical Engineering and Informatics, pp.414-418, 2010. ,
DOI : 10.1109/BMEI.2010.5639551
Analysis of the contour structural irregularity of skin lesions using wavelet decomposition, Pattern Recognition, vol.46, issue.1, pp.98-106, 2013. ,
DOI : 10.1016/j.patcog.2012.07.001
Wavelets and filter banks, SIAM, 1996. ,
A wavelet tour of signal processing. Academic press, 1999. ,
The curvelet transform for image denoising, IEEE Transactions on Image Processing, vol.11, issue.6, pp.670-684, 2002. ,
DOI : 10.1109/TIP.2002.1014998
The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification, 2011 11th International Conference on Hybrid Intelligent Systems (HIS), p.2013 ,
DOI : 10.1109/HIS.2011.6122188
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 : 10.1007/978-1-4612-6333-3
URL : http://europepmc.org/articles/pmc3184888?pdf=render
Partitioning 3d surface meshes using watershed segmentation, IEEE Transactions on Visualization and Computer Graphics, vol.5, issue.4, pp.308-321, 1999. ,
Morphological segmentation, Journal of Visual Communication and Image Representation, vol.1, issue.1, pp.21-46, 1990. ,
DOI : 10.1016/1047-3203(90)90014-M
Improved Watershed Transform for Medical Image Segmentation Using Prior Information, IEEE Transactions on Medical Imaging, vol.23, issue.4, pp.447-458, 2004. ,
DOI : 10.1109/TMI.2004.824224
A new approach for the morphological segmentation of high-resolution satellite imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.2, pp.309-320, 2001. ,
DOI : 10.1109/36.905239
Minimally-supervised morphological segmentation using adaptor grammars, Transactions of the Association for Computational Linguistics, vol.1, pp.255-266, 2013. ,
Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods, Skin Research and Technology, vol.27, issue.1, pp.252-258, 2013. ,
DOI : 10.1016/S0010-4825(97)00020-6
Automated melanoma recognition, IEEE Transactions on Medical Imaging, vol.20, issue.3, pp.233-239, 2001. ,
DOI : 10.1109/42.918473
URL : http://www.emt.tu-graz.ac.at/~pinz/onlinepapers/IEEETMI01.pdf
Lesion detection in dermatoscopic images using anisotropic diffusion and morphological flooding, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), pp.449-453, 1999. ,
DOI : 10.1109/ICIP.1999.817154
A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, pp.273-285, 1985. ,
Unsupervised border detection in dermoscopy images, Skin Research and Technology, vol.1, issue.4, pp.454-462, 2007. ,
DOI : 10.1034/j.1600-0846.2002.00334.x
Border detection in dermoscopy images using hybrid thresholding on optimized color channels, Computerized Medical Imaging and Graphics, vol.35, issue.2, pp.105-115, 2011. ,
DOI : 10.1016/j.compmedimag.2010.08.001
Unified approach for lesion border detection based on mixture modeling and local entropy thresholding, Skin Research and Technology, vol.43, issue.3, pp.314-319, 2013. ,
DOI : 10.1088/0031-9155/43/8/023
Image processing for skin cancer features extraction, International Journal of Scientific & Engineering Research, vol.4, issue.2, pp.1-6, 2013. ,
A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002. ,
DOI : 10.1109/4235.996017
URL : http://work.caltech.edu/amrit/papers/nsga2.ps.gz
Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm, Pattern Recognition, vol.46, issue.3, pp.1012-1019, 2013. ,
DOI : 10.1016/j.patcog.2012.08.012
Image processing, analysis, and machine vision, Cengage Learning, 2014. ,
DOI : 10.1007/978-1-4899-3216-7
Watershed segmentation of dermoscopy images using a watershed technique, Skin Research and Technology, vol.16, issue.3, pp.378-384, 2010. ,
DOI : 10.1111/j.1600-0846.2010.00445.x
URL : http://europepmc.org/articles/pmc3160671?pdf=render
Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images, IEEE Journal of Selected Topics in Signal Processing, vol.3, issue.1, pp.26-34, 2009. ,
DOI : 10.1109/JSTSP.2008.2010631
URL : https://pure.qub.ac.uk/portal/files/589373/IEEE_JSTSP_2009.pdf
Gap-sensitive segmentation and restoration of digital images, TPCG, pp.1-8, 2014. ,
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
,
, Biomedical Imaging: From Nano to Macro 5th IEEE International Symposium on, pp.800-803, 2008.
Global and local information based deep network for skin lesion segmentation, 2017. ,
, , p.142
Analysis of pigmented skin lesion border irregularity using the harmonic wavelet transform, Machine Vision and Image Processing Conference, 2009. ,
, IMVIP'09. 13th International, pp.18-23, 2009.
Harmonic wavelet analysis, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, pp.203-225, 1993. ,
Dermatologistlike feature extraction from skin lesion for improved asymmetry classification in ph 2 database, 2016 IEEE 38th Annual International Conference of the, pp.3855-3858, 2016. ,
Determining the asymmetry of skin lesion with fuzzy borders. Computers in biology and medicine, pp.103-120, 2005. ,
Quantifications of asymmetries on the spectral bands of malignant melanoma using six sigma threshold as preprocessor, Third International Conference on Computational Intelligence and Information Technology (CIIT 2013), pp.80-86, 2013. ,
DOI : 10.1049/cp.2013.2575
Computer-Aided Diagnosis of Skin Lesions Using Conventional Digital Photography: A Reliability and Feasibility Study, PLoS ONE, vol.8, issue.11, p.76212, 2013. ,
DOI : 10.1371/journal.pone.0076212.s003
,
, Melanoma decision support using lighting-corrected intuitive feature models, Computer Vision Techniques for the Diagnosis of Skin Cancer, pp.193-219
Automatic detection of melanoma using broad extraction of features from digital images, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.1357-1360, 2016. ,
DOI : 10.1109/EMBC.2016.7590959
Determination of border irregularity in dermoscopic color images of pigmented skin lesions, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.6459-6462, 2014. ,
DOI : 10.1109/EMBC.2014.6945107
Irregularity index: a new border irregularity measure for cutaneous melanocytic lesions, Medical image analysis, vol.7, issue.1, pp.47-64, 2003. ,
A survey and evaluation of features for the diagnosis of malignant melanoma, 2005. ,
A border irregularity measure using a modified conditional entropy method as a malignant melanoma predictor, International Conference Image Analysis and Recognition, pp.914-921 ,
A new method describing border irregularity of pigmented lesions, Skin Research and Technology, vol.16, issue.1, pp.66-76, 2010. ,
DOI : 10.1111/j.1600-0846.2009.00403.x
Skin lesion classification using relative color features, Skin Research and Technology, vol.12, issue.0, pp.53-64, 2008. ,
DOI : 10.1067/mjd.2001.110395
URL : http://europepmc.org/articles/pmc3184884?pdf=render
Lesion classification using skin patterning, Skin research and technology, vol.6, issue.4, pp.183-192, 2000. ,
A systematic heuristic approach for feature selection for melanoma discrimination using clinical images, Skin Research and Technology, vol.12, issue.5, pp.165-178, 2005. ,
DOI : 10.1067/mjd.2001.110395
URL : http://europepmc.org/articles/pmc3193077?pdf=render
A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images, Computerized Medical Imaging and Graphics, vol.27, issue.5, pp.387-396, 2003. ,
DOI : 10.1016/S0895-6111(03)00030-2
URL : http://europepmc.org/articles/pmc3184460?pdf=render
Colour analysis of skin lesion regions for melanoma discrimination in clinical images, Skin Research and Technology, vol.14, issue.2, pp.94-104, 2003. ,
DOI : 10.1001/archderm.111.10.1291
From colour to tissue histology: Physics-based interpretation of images of pigmented skin lesions, Medical Image Analysis, vol.7, issue.4, pp.489-502, 2003. ,
DOI : 10.1016/S1361-8415(03)00033-1
URL : http://www.cs.bham.ac.uk/~exc/Research/Papers/miccai2002.pdf
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
Computer aided analysis of epi-illumination and transillumination images of skin lesions for diagnosis of skin cancers, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3434-3438, 2011. ,
Multilevel feature extraction for skin lesion segmentation in dermoscopic images, Medical Imaging 2012: Computer-Aided Diagnosis, 2012. ,
DOI : 10.1117/12.911664
, , p.145
Pattern classification of dermoscopy images: A perceptually uniform model, Pattern Recognition, vol.46, issue.1, pp.86-97, 2013. ,
Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images, International Workshop on Machine Learning in Medical Imaging, pp.118-126, 2015. ,
DOI : 10.1109/ICPR.2010.751
URL : https://doi.org/10.1007/978-3-319-24888-2_15
Texture discrimination with multidimensional distributions of signed gray-level differences, Pattern Recognition, vol.34, issue.3, pp.727-739, 2001. ,
DOI : 10.1016/S0031-3203(00)00010-8
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.971-987, 2002. ,
DOI : 10.1109/TPAMI.2002.1017623
Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns, 2015 IEEE International Conference on Image Processing (ICIP), pp.1722-1726, 2015. ,
DOI : 10.1109/ICIP.2015.7351095
Classification of skin cancer images using local binary pattern and SVM classifier, AIP Conference Proceedings, p.80006, 2016. ,
DOI : 10.1023/B:STCO.0000035301.49549.88
URL : https://hal.archives-ouvertes.fr/hal-01423330
Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions, IEEE Transactions on Medical Imaging, vol.32, issue.5, pp.849-861, 2013. ,
DOI : 10.1109/TMI.2013.2239307
Automatic recognition of melanoma using Support Vector Machines: A study based on Wavelet, Curvelet and color features, 2014 International Conference on Industrial Automation, Information and Communications Technology, p.146 ,
DOI : 10.1109/IAICT.2014.6922110
, 2014 International Conference on, pp.70-75, 2014.
Automated prescreening of pigmented skin lesions using standard cameras, Computerized Medical Imaging and Graphics, vol.35, issue.6, pp.481-491, 2011. ,
A deep bag-of-features model for the classification of melanomas in dermoscopy images, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.1369-1372, 2016. ,
DOI : 10.1109/EMBC.2016.7590962
Automated colour identification in melanocytic lesions, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.3021-3024, 2015. ,
DOI : 10.1109/EMBC.2015.7319028
Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp.1055-1058, 2016. ,
DOI : 10.1109/ISBI.2016.7493447
Skin lesion analysis towards melanoma detection using deep learning network. arXiv preprint, 2017. ,
DOI : 10.3390/s18020556
URL : http://www.mdpi.com/1424-8220/18/2/556/pdf
Deep bayesian active learning with image data. arXiv preprint, 2017. ,
Skin Lesion Classification from Dermoscopic Images Using Deep Learning Techniques, Biomedical Engineering, pp.49-54, 2017. ,
DOI : 10.2316/P.2017.852-053
Dermatologist-level classification of skin cancer with deep neural networks, Nature, issue.7639, p.542115, 2017. ,
DOI : 10.1038/nature21056
Deep learning ensembles for melanoma recognition in dermoscopy images, IBM Journal of Research and Development, vol.61, issue.4, pp.5-6, 2017. ,
Text categorization with Support Vector Machines: Learning with many relevant features, European conference on machine learning, pp.137-142, 1998. ,
DOI : 10.1007/BFb0026683
URL : http://ranger.uta.edu/~alp/ix/readings/SVMsforTextCategorization.pdf
Artificial neural networks, PHI Learning Pvt. Ltd, 2009. ,
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
Improving dermoscopy image classification using color constancy, IEEE journal of biomedical and health informatics, vol.19, issue.3, pp.1146-1152, 2015. ,
Size Functions for the Morphological Analysis of Melanocytic Lesions, International Journal of Biomedical Imaging, vol.25, issue.6, 2010. ,
DOI : 10.1159/000018308
URL : http://downloads.hindawi.com/journals/ijbi/2010/621357.pdf
Different Learning Paradigms for the Classification of Melanoid Skin Lesions Using Wavelets, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3136-3139, 2007. ,
DOI : 10.1109/IEMBS.2007.4352994
Characterization of digital medical images utilizing support vector machines, BMC Medical Informatics and Decision Making, vol.4, issue.11, 2004. ,
Supervised learning of melanocytic skin lesion images, 2008 Conference on Human System Interactions, pp.121-125, 2008. ,
DOI : 10.1109/HSI.2008.4581420
Learning methods for melanoma recognition, International Journal of Imaging Systems and Technology, vol.27, issue.4, pp.316-322, 2010. ,
DOI : 10.1148/radiology.143.1.7063747
An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm, Computerized Medical Imaging and Graphics, vol.32, issue.7, pp.566-579, 2008. ,
DOI : 10.1016/j.compmedimag.2008.06.005
A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions, Journal of Biomedical Informatics, vol.34, issue.1, pp.28-36, 2001. ,
DOI : 10.1006/jbin.2001.1004
Ph 2-a dermoscopic image database for research and benchmarking, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.5437-5440, 2013. ,
Interactive atlas of dermoscopy (book and cd-rom, 2000. ,
Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers, International Workshop on Machine Learning in Medical Imaging, pp.164-171, 2016. ,
DOI : 10.1007/s11263-015-0816-y
Mumford and shah model and its applications to image segmentation and image restoration, Handbook of Mathematical Methods in Imaging, pp.1-52, 2014. ,
DOI : 10.1007/978-0-387-92920-0_25
, , p.149
, Some fast projection methods based on chan-vese model for image segmentation, EURASIP Journal on Image and Video Processing, vol.2014, issue.1, p.7, 2014.
Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear processes in geophysics, pp.561-566, 2004. ,
URL : https://hal.archives-ouvertes.fr/hal-00302394
Comparison between fourier and wavelets transforms in biospeckle signals, Applied Mathematics, 2013. ,
Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape, SIAM Journal on Mathematical Analysis, vol.15, issue.4, pp.723-736, 1984. ,
DOI : 10.1137/0515056
The finite ridgelet transform for image representation, IEEE Transactions on image Processing, vol.12, issue.1, pp.16-28, 2003. ,
Ridgelets: A key to higherdimensional intermittency?, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol.357, pp.2495-2509, 1760. ,
Curvelets: A surprisingly effective nonadaptive representation for objects with edges, 2000. ,
Fast Discrete Curvelet Transforms, Multiscale Modeling & Simulation, vol.5, issue.3, pp.861-899, 2006. ,
DOI : 10.1137/05064182X
Curvelet Based Feature Extraction, 2010. ,
DOI : 10.5772/8940
, , p.150
, local binary patterns: Application to face recognition, IEEE transactions on pattern analysis and machine intelligence, vol.28, issue.12, pp.2037-2041, 2006.
Haralick feature extraction from LBP images for color texture classification, 2008 First Workshops on Image Processing Theory, Tools and Applications, pp.1-8, 2008. ,
DOI : 10.1109/IPTA.2008.4743780
URL : https://hal.archives-ouvertes.fr/hal-01937686
Description of Interest Regions with Center-Symmetric Local Binary Patterns, ICVGIP, pp.58-69, 2006. ,
DOI : 10.1007/11949619_6
Face detection based on multi-block lbp representation Advances in biometrics, pp.11-18, 2007. ,
Descriptor based methods in the wild In Workshop on faces in'real-life'images: Detection, alignment, and recognition, 2008. ,
Computer vision using local binary patterns, 2011. ,
Statistical pattern recognition: A review, IEEE Transactions on pattern analysis and machine intelligence, vol.22, issue.1, pp.4-37, 2000. ,
A tutorial on support vector regression, Statistics and computing, vol.14, issue.3, pp.199-222, 2004. ,
A comprehensive foundation, Neural Networks, vol.2, 2004. ,
Artificial neural networks. handbook of measuring system design, 2005. ,
A brief Introduction to Neural Networks. dkriesel.com, 2005. ,
Handbook of measuring system design, 2005. ,
A study of cross-validation and bootstrap for accuracy estimation and model selection, Ijcai, pp.1137-1145, 1995. ,
-fold cross-validation and the repeated learning-testing methods, Biometrika, vol.76, issue.3, pp.503-514, 1989. ,
DOI : 10.1093/biomet/76.3.503
A stochastic-variational model for soft mumford-shah segmentation, International Journal of Biomedical Imaging, 2006. ,
PH2: A Public Database for the Analysis of Dermoscopic Images, Dermoscopy Image Analysis (M. E. Celebi, T. Mendonca, and J. S ,
DOI : 10.1201/b19107-14
, , pp.419-439, 2015.
An efficient local Chan???Vese model for image segmentation, Pattern Recognition, vol.43, issue.3, pp.603-618, 2010. ,
DOI : 10.1016/j.patcog.2009.08.002
Rate of Growth in Melanomas, Archives of Dermatology, vol.142, issue.12, pp.1551-1558, 2006. ,
DOI : 10.1001/archderm.142.12.1551
Multifocus image fusion by combining curvelet and wavelet transform, Pattern Recognition Letters, vol.29, issue.9, pp.1295-1301, 2008. ,
DOI : 10.1016/j.patrec.2008.02.002
Curvelets and ridgelets, 2009. ,