A survey on automated melanoma detection

Abstract Skin cancer is defined as the rapid growth of skin cells due to DNA damage that cannot be repaired. Melanoma is one of the deadliest types of skin cancer, which originates from melanocytes. While other skin cancer types have limited spreading capabilities, the danger of melanoma comes from its ability to spread (metastasize) rapidly. Fortunately, it can be detected by visual inspection of the skin surface, and it is 100% curable if identified in the early stages. However, detection by “subjective visual inspection” creates an important problem, due to investigators’ different levels of experiences and education. Dermoscopy (dermatoscopy) has significantly increased the diagnostic accuracy of melanoma since late 90’s. In addition, several systems have been proposed in order to assist investigators or to perform an automatic melanoma detection. This survey focuses on the algorithms for automated melanoma detection in dermoscopic images through an extensive analysis of the stages in methodologies proposed in the literature, and by examining related concepts and describing possible future directions through open problems in this domain of research.

[1]  Mao-Jiun J. Wang,et al.  Image thresholding by minimizing the measures of fuzzines , 1995, Pattern Recognit..

[2]  Josep Malvehy,et al.  Dermoscopy report: proposal for standardization. Results of a consensus meeting of the International Dermoscopy Society. , 2007, Journal of the American Academy of Dermatology.

[3]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

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

[5]  P. Bühlmann,et al.  The group lasso for logistic regression , 2008 .

[6]  Farid Melgani,et al.  Robust image binarization with ensembles of thresholding algorithms , 2006, J. Electronic Imaging.

[7]  Michael Elad,et al.  Image Sequence Denoising via Sparse and Redundant Representations , 2009, IEEE Transactions on Image Processing.

[8]  Witold Pedrycz,et al.  Type-2 Fuzzy Logic: Theory and Applications , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[9]  Junzhou Huang,et al.  The Benefit of Group Sparsity , 2009 .

[10]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[11]  Shehzad Khalid,et al.  Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques , 2015, Australasian Physical & Engineering Sciences in Medicine.

[12]  R. H. Moss,et al.  Neural network diagnosis of malignant melanoma from color images , 1994, IEEE Transactions on Biomedical Engineering.

[13]  J. Emery,et al.  Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review , 2015, British Journal of Dermatology.

[14]  Junzhou Huang,et al.  Learning with structured sparsity , 2009, ICML '09.

[15]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

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

[17]  Jorge S. Marques,et al.  Improving Dermoscopy Image Classification Using Color Constancy , 2015, IEEE Journal of Biomedical and Health Informatics.

[18]  Hao Chen,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.

[19]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[20]  Yichuan Tang,et al.  Deep Learning using Linear Support Vector Machines , 2013, 1306.0239.

[21]  Hao Chen,et al.  DCAN: Deep contour‐aware networks for object instance segmentation from histology images , 2017, Medical Image Anal..

[22]  I. Kononenko,et al.  INDUCTION OF DECISION TREES USING RELIEFF , 1995 .

[23]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[24]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

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

[26]  Engin Senel,et al.  Dermatoscopy of non-melanocytic skin tumors. , 2011, Indian journal of dermatology, venereology and leprology.

[27]  Jorge S. Marques,et al.  Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features , 2014, IEEE Systems Journal.

[28]  Masafumi Hagiwara,et al.  Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system , 2006, Melanoma research.

[29]  A. Mathai,et al.  Understanding and using sensitivity, specificity and predictive values , 2008, Indian journal of ophthalmology.

[30]  Kevin L. Priddy,et al.  Artificial neural networks - an introduction , 2005, Tutorial text series.

[31]  A. Kalloo,et al.  Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images , 2018, Journal of the American Academy of Dermatology.

[32]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[33]  Mehmet Türkan,et al.  A review of sparsity-based clustering methods , 2018, Signal Process..

[34]  Tim K. Lee,et al.  Determining the asymmetry of skin lesion with fuzzy borders , 2005, Comput. Biol. Medicine.

[35]  Michael Elad,et al.  Learning Multiscale Sparse Representations for Image and Video Restoration , 2007, Multiscale Model. Simul..

[36]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[37]  Michael Elad,et al.  Compression of facial images using the K-SVD algorithm , 2008, J. Vis. Commun. Image Represent..

[38]  Qaisar Abbas,et al.  A perceptually oriented method for contrast enhancement and segmentation of dermoscopy images , 2013, 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.

[39]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[40]  Neal Rosen,et al.  Targeted cancer therapies , 2011, Chinese journal of cancer.

[41]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[42]  Robert P Dellavalle,et al.  Update on mobile applications in dermatology. , 2014, Dermatology online journal.

[43]  Pierre Vandergheynst,et al.  Image compression using an edge adapted redundant dictionary and wavelets , 2006, Signal Process..

[44]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[45]  M. Emin Yüksel,et al.  Accurate Segmentation of Dermoscopic Images by Image Thresholding Based on Type-2 Fuzzy Logic , 2009, IEEE Transactions on Fuzzy Systems.

[46]  Jordan V. Wang,et al.  Challenges to smartphone applications for melanoma detection. , 2017, Dermatology online journal.

[47]  Mutlu Mete,et al.  Fast density-based lesion detection in dermoscopy images , 2011, Comput. Medical Imaging Graph..

[48]  Angela Ferrari,et al.  Interactive atlas of dermoscopy , 2000 .

[49]  Mohamed-Jalal Fadili,et al.  Inpainting and Zooming Using Sparse Representations , 2009, Comput. J..

[50]  Maciej Ogorzalek,et al.  Modern Techniques for Computer-Aided Melanoma Diagnosis , 2011 .

[51]  Yonina C. Eldar,et al.  Robust Recovery of Signals From a Structured Union of Subspaces , 2008, IEEE Transactions on Information Theory.

[52]  J. Wolf,et al.  Diagnostic inaccuracy of smartphone applications for melanoma detection. , 2013, JAMA dermatology.

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

[54]  Mohammed Bennamoun,et al.  3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[55]  Dennis T. Leaver,et al.  Principles and Practice of Radiation Therapy , 2003 .

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

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

[58]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[59]  Tim K. Lee,et al.  MEASURING BORDER IRREGULARITY AND SHAPE OF CUTANEOUS MELANOCYTIC LESIONS , 2001 .

[60]  R. Real,et al.  The Probabilistic Basis of Jaccard's Index of Similarity , 1996 .

[61]  Bram van Ginneken,et al.  Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks , 2016, IEEE Transactions on Medical Imaging.

[62]  A. Tenenhaus,et al.  Detection of melanoma from dermoscopic images of naevi acquired under uncontrolled conditions , 2010, 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.

[63]  Ning Situ,et al.  A narrow band graph partitioning method for skin lesion segmentation , 2009, Pattern Recognit..

[64]  A. Marghoob,et al.  Breslow thickness and Clark level in melanoma , 2000, Cancer.

[65]  R. Wolfe,et al.  Comparative performance of 4 dermoscopic algorithms by nonexperts for the diagnosis of melanocytic lesions. , 2005, Archives of dermatology.

[66]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[67]  James Zijun Wang,et al.  Automatic diagnosis of melanoma using machine learning methods on a spectroscopic system , 2014, BMC Medical Imaging.

[68]  Rita Cucchiara,et al.  Comparison of color clustering algorithms for segmentation of dermatological images , 2006, SPIE Medical Imaging.

[69]  Peter Shirley,et al.  oRGB: A Practical Opponent Color Space for Computer Graphics , 2009, IEEE Computer Graphics and Applications.

[70]  J. Tasic,et al.  Colour spaces: perceptual, historical and applicational background , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[71]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[72]  Torello Lotti,et al.  European Handbook of Dermatological Treatments , 2000, Springer Berlin Heidelberg.

[73]  R. Dellavalle,et al.  Mobile applications in dermatology. , 2013, JAMA dermatology.

[74]  M. G. Fleming,et al.  Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. , 2003, Journal of the American Academy of Dermatology.

[75]  László Neumann,et al.  A novel method for color correction in epiluminescence microscopy , 2011, Comput. Medical Imaging Graph..

[76]  Pietro Rubegni,et al.  Automated diagnosis of pigmented skin lesions , 2002, International journal of cancer.

[77]  Gerald Schaefer,et al.  Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods , 2013, 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.

[78]  P. Schmid.  Segmentation of digitized dermatoscopic images by two-dimensional color clustering , 1999, IEEE Transactions on Medical Imaging.

[79]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[80]  Gabriel Peyré,et al.  Sparse Modeling of Textures , 2009, Journal of Mathematical Imaging and Vision.

[81]  T Lee,et al.  Dullrazor®: A software approach to hair removal from images , 1997, Comput. Biol. Medicine.

[82]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[83]  Marios Anthimopoulos,et al.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[84]  Robert H. Barbour,et al.  Automated melanoma diagnosis: where are we at? , 2000, 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.

[85]  Gerald Schaefer,et al.  An improved objective evaluation measure for border detection in dermoscopy images , 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.

[86]  Tim K. Lee,et al.  Chromatic aberration correction: an enhancement to the calibration of low‐cost digital dermoscopes , 2011, 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.

[87]  Harold S Rabinovitz,et al.  Dermoscopy of pigmented skin lesions. , 2005, Journal of the American Academy of Dermatology.

[88]  Jorge S. Marques,et al.  Accurate and Scalable System for Automatic Detection of Malignant Melanoma , 2015 .