Computational methods for the image segmentation of pigmented skin lesions: A review

BACKGROUND AND OBJECTIVES Because skin cancer affects millions of people worldwide, computational methods for the segmentation of pigmented skin lesions in images have been developed in order to assist dermatologists in their diagnosis. This paper aims to present a review of the current methods, and outline a comparative analysis with regards to several of the fundamental steps of image processing, such as image acquisition, pre-processing and segmentation. METHODS Techniques that have been proposed to achieve these tasks were identified and reviewed. As to the image segmentation task, the techniques were classified according to their principle. RESULTS The techniques employed in each step are explained, and their strengths and weaknesses are identified. In addition, several of the reviewed techniques are applied to macroscopic and dermoscopy images in order to exemplify their results. CONCLUSIONS The image segmentation of skin lesions has been addressed successfully in many studies; however, there is a demand for new methodologies in order to improve the efficiency.

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

[2]  Taís Facina,et al.  Estimativa 2014 – Incidência de Câncer no Brasil , 2014 .

[3]  Qaisar Abbas,et al.  Unified approach for lesion border detection based on mixture modeling and local entropy thresholding , 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.

[4]  M S Woolfson,et al.  Application of region-based segmentation and neural network edge detection to skin lesions. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[5]  Mark E. Roberts,et al.  An Artificially Evolved Vision System for Segmenting Skin Lesion Images , 2003, MICCAI.

[6]  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.

[7]  Masaru Tanaka,et al.  Three‐phase general border detection method for dermoscopy images using non‐uniform illumination correction , 2012, 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.

[8]  Xin Jin,et al.  Mean Shift , 2017, Encyclopedia of Machine Learning and Data Mining.

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

[10]  H S Ganzeli,et al.  SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques , 2011, IEEE Latin America Transactions.

[11]  James Bailey,et al.  Computer-Aided Diagnosis of Melanoma Using Border- and Wavelet-Based Texture Analysis , 2012, IEEE Transactions on Information Technology in Biomedicine.

[12]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[13]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

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

[16]  Hassan A. Kingravi,et al.  Nonlinear vector filtering for impulsive noise removal from color images , 2007, J. Electronic Imaging.

[17]  Volodymyr I. Ponomaryov,et al.  Wavelet Transform Fuzzy Algorithms for Dermoscopic Image Segmentation , 2012, Comput. Math. Methods Medicine.

[18]  Ghassan Hamarneh,et al.  Hair Enhancement in Dermoscopic Images Using Dual-Channel Quaternion Tubularness Filters and MRF-Based Multilabel Optimization , 2014, IEEE Transactions on Image Processing.

[19]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[20]  Junji Maeda,et al.  Number-Driven Perceptual Segmentation of Natural Color Images for Easy Decision of Optimal Result , 2007, 2007 IEEE International Conference on Image Processing.

[21]  Randy H. Moss,et al.  Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes , 2005, 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.

[22]  J. Scharcanski,et al.  Computer Vision Techniques for the Diagnosis of Skin Cancer , 2013 .

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

[24]  William K. Pratt,et al.  Digital Image Processing: PIKS Inside , 2001 .

[25]  Koichi Ogawa,et al.  Development of a novel border detection method for melanocytic and non-melanocytic dermoscopy images , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[26]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

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

[28]  T. Chan,et al.  A Variational Level Set Approach to Multiphase Motion , 1996 .

[29]  Gerald Schaefer,et al.  Contrast enhancement in dermoscopy images by maximizing a histogram bimodality measure , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[30]  Junji Maeda,et al.  Unsupervised Perceptual Segmentation of Natural Color Images Using Fuzzy-Based Hierarchical Algorithm , 2007, SCIA.

[31]  Frank Nielsen,et al.  Statistical region merging , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Bo Thiesson,et al.  Image and Video Segmentation by Anisotropic Kernel Mean Shift , 2004, ECCV.

[33]  Shi-Yin Qin,et al.  PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma , 2009, Comput. Medical Imaging Graph..

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

[35]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[36]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[37]  Claude L. Fennema,et al.  Scene Analysis Using Regions , 1970, Artif. Intell..

[38]  Qaisar Abbas,et al.  Unsupervised skin lesions border detection via two-dimensional image analysis , 2011, Comput. Methods Programs Biomed..

[39]  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.

[40]  Célia A. Zorzo Barcelos,et al.  An automatic based nonlinear diffusion equations scheme for skin lesion segmentation , 2009, Appl. Math. Comput..

[41]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Allen R. Tannenbaum,et al.  Localizing Region-Based Active Contours , 2008, IEEE Transactions on Image Processing.

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

[44]  Clement T. Yu,et al.  Segmentation of skin cancer images , 1999, Image Vis. Comput..

[45]  G. Castellano,et al.  Neuro-fuzzy analysis of dermatological images , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

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

[47]  Rafael C. Gonzales,et al.  Digital Image Processing -3/E. , 2012 .

[48]  Qaisar Abbas,et al.  Hair removal methods: A comparative study for dermoscopy images , 2011, Biomed. Signal Process. Control..

[49]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[50]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[51]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[52]  Jorge S. Marques,et al.  Improving dermoscopy image analysis using color constancy , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[53]  Y. Suzuki,et al.  Perceptual image segmentation using fuzzy-based hierarchical algorithm and its application to dermoscopy images , 2008, 2008 IEEE Conference on Soft Computing in Industrial Applications.

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

[55]  Niklas Nordström.  Biased Anisotropic Diffusion - A Unified Regularization and Diffusion Approach to Edge Detection , 1990, ECCV.

[56]  Jorge S. Marques,et al.  Dermoscopy Image Analysis , 2015 .

[57]  Thomas S. Huang,et al.  Image processing , 1971 .

[58]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[59]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[60]  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.

[61]  James M. Rehg,et al.  Feature-preserving artifact removal from dermoscopy images , 2008, SPIE Medical Imaging.

[62]  Tony F. Chan,et al.  Active Contours without Edges for Vector-Valued Images , 2000, J. Vis. Commun. Image Represent..

[63]  Timoteo Carletti,et al.  The Stochastic Evolution of a Protocell: The Gillespie Algorithm in a Dynamically Varying Volume , 2011, Comput. Math. Methods Medicine.

[64]  Jacob Scharcanski,et al.  Macroscopic Pigmented Skin Lesion Segmentation and Its Influence on Lesion Classification and Diagnosis , 2013 .

[65]  Bipin C. Desai,et al.  A multiple expert-based melanoma recognition system for dermoscopic images of pigmented skin lesions , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[66]  Maurílio Boaventura,et al.  A well-balanced flow equation for noise removal and edge detection , 2003, IEEE Trans. Image Process..

[67]  Qaisar Abbas,et al.  Lesion border detection in dermoscopy images using dynamic programming , 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.

[68]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[69]  Jacob Scharcanski,et al.  A coarse-to-fine approach for segmenting melanocytic skin lesions in standard camera images , 2013, Comput. Methods Programs Biomed..

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

[71]  G. Argenziano,et al.  Noninvasive Imaging of Skin Tumors , 2004, Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.].

[72]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[73]  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.

[74]  Zhen Ma,et al.  A Novel Approach to Segment Skin Lesions in Dermoscopic Images Based on a Deformable Model , 2016, IEEE Journal of Biomedical and Health Informatics.

[75]  H G Adelmann,et al.  Butterworth equations for homomorphic filtering of images , 1998, Comput. Biol. Medicine.

[76]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

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

[78]  Gomes Pinheiro,et al.  Skin melanoma segmentation by morphological approach , 2012, ICACCI '12.

[79]  Prasanna K. Sahoo,et al.  Threshold selection using Renyi's entropy , 1997, Pattern Recognit..

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

[81]  Alfredo Paolillo,et al.  Automatic Diagnosis of Melanoma: A Software System Based on the 7-Point Check-List , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[82]  Margaret Oliviero,et al.  Current technologies for the in vivo diagnosis of cutaneous melanomas. , 2004, Clinics in dermatology.

[83]  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.

[84]  Qaisar Abbas,et al.  Skin tumor area extraction using an improved dynamic programming approach , 2012, 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]  Junji Maeda,et al.  Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.

[86]  Xuelong Li,et al.  Mean shift based gradient vector flow for image segmentation , 2013, Comput. Vis. Image Underst..

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

[88]  David Zhang,et al.  Dark line detection with line width extraction , 2008, 2008 15th IEEE International Conference on Image Processing.

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

[90]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[91]  Gerald Schaefer,et al.  Skin lesion segmentation using an improved snake model , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

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

[93]  Milan Sonka,et al.  Image pre-processing , 1993 .

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

[95]  Gerald Schaefer,et al.  Colour and contrast enhancement for improved skin lesion segmentation , 2011, Comput. Medical Imaging Graph..

[96]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[97]  Sheila MacNeil,et al.  State of the art in non‐invasive imaging of cutaneous melanoma , 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.