Automated prescreening of melanocytic skin lesions using standard camera images.

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[1]  T. Trowbridge,et al.  Average irregularity representation of a rough surface for ray reflection , 1975 .

[2]  Alvy Ray Smith,et al.  Color gamut transform pairs , 1978, SIGGRAPH.

[3]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[4]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[5]  J. C. van der Leun,et al.  FORWARD SCATTERING PROPERTIES OF HUMAN EPIDERMAL LAYERS , 1984, Photochemistry and photobiology.

[6]  K Wolff,et al.  In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions. , 1987, Journal of the American Academy of Dermatology.

[7]  W. Stolz,et al.  The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. , 1994, Journal of the American Academy of Dermatology.

[8]  W Abmayr,et al.  Evaluation of different image acquisition techniques for a computer vision system in the diagnosis of malignant melanoma. , 1994, Journal of the American Academy of Dermatology.

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[10]  W. Stolz,et al.  Can early malignant melanoma be differentiated from atypical melanocytic nevi by in vivo techniques? , 1997, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[11]  J. Mayer,et al.  Systematic review of the diagnostic accuracy of dermatoscopy in detecting malignant melanoma , 1997, The Medical journal of Australia.

[12]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[13]  V. Tuchin Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis , 2000 .

[14]  Robert M. Haralick,et al.  Probabilistic vs. geometric similarity measures for image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[15]  Harald Ganster,et al.  Automated Melanoma Recognition , 2001, IEEE Trans. Medical Imaging.

[16]  Lucila Ohno-Machado,et al.  A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions , 2001, J. Biomed. Informatics.

[17]  R. Bakos,et al.  Sunburn, sunscreens, and phenotypes: some risk factors for cutaneous melanoma in southern Brazil , 2002, International journal of dermatology.

[18]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Vladimir Vezhnevets,et al.  A Survey on Pixel-Based Skin Color Detection Techniques , 2003 .

[20]  Gladimir V. G. Baranoski,et al.  A Biophysically‐Based Spectral Model of Light Interaction with Human Skin , 2004, Comput. Graph. Forum.

[21]  Riccardo Bono,et al.  Melanoma Computer-Aided Diagnosis , 2004, Clinical Cancer Research.

[22]  I Zalaudek,et al.  Which is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology? , 2004, The British journal of dermatology.

[23]  M. Pollán,et al.  Cutaneous melanoma: hints from occupational risks by anatomic site in Swedish men , 2004, Occupational and Environmental Medicine.

[24]  J. Crowley,et al.  Estimating Face orientation from Robust Detection of Salient Facial Structures , 2004 .

[25]  Michael Binder,et al.  Limitations of dermoscopy in the recognition of melanoma. , 2005, Archives of dermatology.

[26]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[27]  Evgenia I. Tsompanaki,et al.  A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit. A preliminary report , 2006, International journal of dermatology.

[28]  J. Whited,et al.  Teledermatology research review , 2006, International journal of dermatology.

[29]  T Fikrle,et al.  Digital computer analysis of dermatoscopical images of 260 melanocytic skin lesions; perimeter/area ratio for the differentiation between malignant melanomas and melanocytic nevi , 2007, Journal of the European Academy of Dermatology and Venereology : JEADV.

[30]  Hans Peter Soyer,et al.  Multispectral Image Analysis , 2007 .

[31]  R. Hofmann-Wellenhof,et al.  Clinical Examination of Melanocytic Neoplasms Including ABCDE Criteria , 2007 .

[32]  W. Stoecker,et al.  Unsupervised border detection in dermoscopy images , 2007, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[33]  Randy H. Moss,et al.  A methodological approach to the classification of dermoscopy images , 2007, Comput. Medical Imaging Graph..

[34]  R. H. Moss,et al.  Border detection in dermoscopy images using statistical region merging , 2008, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[35]  Efthimios Kaxiras,et al.  Melanin absorption spectroscopy: new method for noninvasive skin investigation and melanoma detection. , 2008, Journal of biomedical optics.

[36]  Masafumi Hagiwara,et al.  An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm , 2008, Comput. Medical Imaging Graph..

[37]  D. Duffy,et al.  The Queensland Study of Melanoma: Environmental and Genetic Associations (Q-MEGA); Study Design, Baseline Characteristics, and Repeatability of Phenotype and Sun Exposure Measures , 2008, Twin Research and Human Genetics.

[38]  Giuseppe Argenziano,et al.  Digital image analysis for diagnosis of skin tumors. , 2008, Seminars in cutaneous medicine and surgery.

[39]  D. Ruiz,et al.  A cooperative approach for the diagnosis of the melanoma , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[40]  Rainer Hofmann-Wellenhof,et al.  Teledermatology: an update. , 2008, Seminars in cutaneous medicine and surgery.

[41]  Constantine Butakoff,et al.  Independent Histogram Pursuit for Segmentation of Skin Lesions , 2008, IEEE Transactions on Biomedical Engineering.

[42]  J. Pawelek,et al.  Why do melanomas get so dark? , 2009, Experimental dermatology.

[43]  Renato Marchesini,et al.  In vivo characterization of melanin in melanocytic lesions: spectroscopic study on 1671 pigmented skin lesions. , 2009, Journal of biomedical optics.

[44]  Gerard de Haan,et al.  Automatic imaging sysem with decision support for inspection of pigmented skin lesions and melanoma diagnosis. , 2009 .

[45]  M. Stella Atkins,et al.  A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting , 2009, MICCAI.

[46]  Karsten König,et al.  Spectral fluorescence lifetime detection and selective melanin imaging by multiphoton laser tomography for melanoma diagnosis , 2009, Experimental dermatology.

[47]  Gerald Schaefer,et al.  Lesion border detection in dermoscopy images , 2009, Comput. Medical Imaging Graph..

[48]  Junji Maeda,et al.  Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.

[49]  Gerald Schaefer,et al.  Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.

[50]  Jinshan Tang A multi-direction GVF snake for the segmentation of skin cancer images , 2009, Pattern Recognit..

[51]  Ilias Maglogiannis,et al.  Overview of Advanced Computer Vision Systems for Skin Lesions Characterization , 2009, IEEE Transactions on Information Technology in Biomedicine.

[52]  Ali Esteki,et al.  Extraction of skin lesion texture features based on independent component analysis , 2009, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[53]  H. Peter Soyer,et al.  Scanning for melanoma , 2010 .

[54]  Renato Marchesini,et al.  In Vivo Optical Properties of Melanocytic Skin Lesions: Common Nevi, Dysplastic Nevi and Malignant Melanoma , 2010, Photochemistry and photobiology.

[55]  Jacob Scharcanski,et al.  Shading Attenuation in Human Skin Color Images , 2010, ISVC.

[56]  Jacob Scharcanski,et al.  Pigmented skin lesion segmentation on macroscopic images , 2010, 2010 25th International Conference of Image and Vision Computing New Zealand.

[57]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[58]  Cláudio Rosito Jung,et al.  Semi-automated Diagnosis of Melanoma through the Analysis of Dermatological Images , 2010, 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images.

[59]  Mohammad Aldeen,et al.  Border detection in dermoscopy images using hybrid thresholding on optimized color channels , 2011, Comput. Medical Imaging Graph..

[60]  David I. McLean,et al.  Generalizing Common Tasks in Automated Skin Lesion Diagnosis , 2011, IEEE Transactions on Information Technology in Biomedicine.

[61]  R. Joe Stanley,et al.  Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images , 2011, Comput. Medical Imaging Graph..

[62]  Jacob Scharcanski,et al.  An ICA-based method for the segmentation of pigmented skin lesions in macroscopic images , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[63]  Rui Hu,et al.  Implementation of the 7-point checklist for melanoma detection on smart handheld devices , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[64]  Warren S Warren,et al.  Pump-Probe Imaging Differentiates Melanoma from Melanocytic Nevi , 2011, Science Translational Medicine.

[65]  Paul W. Fieguth,et al.  Automatic Skin Lesion Segmentation via Iterative Stochastic Region Merging , 2011, IEEE Transactions on Information Technology in Biomedicine.

[66]  Antonio Soriano Payá,et al.  A decision support system for the diagnosis of melanoma: A comparative approach , 2011, Expert Syst. Appl..

[67]  Jacob Scharcanski,et al.  Automated prescreening of pigmented skin lesions using standard cameras , 2011, Comput. Medical Imaging Graph..

[68]  Gerald Schaefer,et al.  Gradient vector flow with mean shift for skin lesion segmentation , 2011, Comput. Medical Imaging Graph..

[69]  R. Bonfá,et al.  A precocidade diagnóstica do melanoma cutâneo: uma observação no sul do Brasil , 2011 .

[70]  Huiyu Zhou,et al.  Age classification using Radon transform and entropy based scaling SVM , 2011, BMVC.

[71]  Louis D. Silverstein,et al.  Observer Performance Using Virtual Pathology Slides: Impact of LCD Color Reproduction Accuracy , 2012, Journal of Digital Imaging.

[72]  F Piccinini,et al.  Multi‐image based method to correct vignetting effect in light microscopy images , 2012, Journal of microscopy.

[73]  David A. Clausi,et al.  Illumination correction in dermatological photographs using multi-stage illumination modeling for skin lesion analysis , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[74]  Rafael García,et al.  Computerized analysis of pigmented skin lesions: A review , 2012, Artif. Intell. Medicine.

[75]  Robert B. Fisher,et al.  Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[76]  Robert B. Fisher,et al.  A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions , 2013 .

[77]  David A. Clausi,et al.  MSIM: Multistage Illumination Modeling of Dermatological Photographs for Illumination-Corrected Skin Lesion Analysis , 2013, IEEE Transactions on Biomedical Engineering.

[78]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .