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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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General Review Article

Deep Learning Approaches Towards Skin Lesion Segmentation and Classification from Dermoscopic Images - A Review

Author(s): Ramsha Baig, Maryam Bibi, Anmol Hamid, Sumaira Kausar* and Shahzad Khalid

Volume 16, Issue 5, 2020

Page: [513 - 533] Pages: 21

DOI: 10.2174/1573405615666190129120449

Price: $65

Abstract

Background: Automated intelligent systems for unbiased diagnosis are primary requirement for the pigment lesion analysis. It has gained the attention of researchers in the last few decades. These systems involve multiple phases such as pre-processing, feature extraction, segmentation, classification and post processing. It is crucial to accurately localize and segment the skin lesion. It is observed that recent enhancements in machine learning algorithms and dermoscopic techniques reduced the misclassification rate therefore, the focus towards computer aided systems increased exponentially in recent years. Computer aided diagnostic systems are reliable source for dermatologists to analyze the type of cancer, but it is widely acknowledged that even higher accuracy is needed for computer aided diagnostic systems to be adopted practically in the diagnostic process of life threatening diseases.

Introduction: Skin cancer is one of the most threatening cancers. It occurs by the abnormal multiplication of cells. The core three types of skin cells are: Squamous, Basal and Melanocytes. There are two wide classes of skin cancer; Melanocytic and non-Melanocytic. It is difficult to differentiate between benign and malignant melanoma, therefore dermatologists sometimes misclassify the benign and malignant melanoma. Melanoma is estimated as 19th most frequent cancer, it is riskier than the Basel and Squamous carcinoma because it rapidly spreads throughout the body. Hence, to lower the death risk, it is critical to diagnose the correct type of cancer in early rudimentary phases. It can occur on any part of body, but it has higher probability to occur on chest, back and legs.

Methods: The paper presents a review of segmentation and classification techniques for skin lesion detection. Dermoscopy and its features are discussed briefly. After that Image pre-processing techniques are described. A thorough review of segmentation and classification phases of skin lesion detection using deep learning techniques is presented Literature is discussed and a comparative analysis of discussed methods is presented.

Conclusion: In this paper, we have presented the survey of more than 100 papers and comparative analysis of state of the art techniques, model and methodologies. Malignant melanoma is one of the most threating and deadliest cancers. Since the last few decades, researchers are putting extra attention and effort in accurate diagnosis of melanoma. The main challenges of dermoscopic skin lesion images are: low contrasts, multiple lesions, irregular and fuzzy borders, blood vessels, regression, hairs, bubbles, variegated coloring and other kinds of distortions. The lack of large training dataset makes these problems even more challenging. Due to recent advancement in the paradigm of deep learning, and specially the outstanding performance in medical imaging, it has become important to review the deep learning algorithms performance in skin lesion segmentation. Here, we have discussed the results of different techniques on the basis of different evaluation parameters such as Jaccard coefficient, sensitivity, specificity and accuracy. And the paper listed down the major achievements in this domain with the detailed discussion of the techniques. In future, it is expected to improve results by utilizing the capabilities of deep learning frameworks with other pre and post processing techniques so reliable and accurate diagnostic systems can be built.

Keywords: Skin lesion, melanoma, segmentation, deep learning, convolution neural network, skin cancer.

Graphical Abstract

[1]
Melanoma-Skin Cancer, 2016. [Online] Available from:. http://www.skincancer.org/skin-cancer-information/
[2]
Sebe N, Cohen I, Garg A, Huang TS. Machine learning in computer vision. 1st ed. Netherlands: Springer 2005.
[3]
Zhao R, Ouyang W, Li H, Wang X. Saliency detection by multicon text deep learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, New Jersey: IEEE 2015; pp. 1265-74.
[4]
Melinščak M, Prentašić P, Lončarić S. Retinal vessel segmentation using deep neural networks. In: 10th International Conference on Computer Vision Theory and Applications. Berlin, Germany 2015; pp. 577-82.
[5]
Pereira S, Pinto A, Alves V, Silva C. Deep convolutional neural‎ networks for the segmentation of gliomas in multi-sequence‎ MRI. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; Munich, Germany. Berlin: Springer 2016; pp. 131-43.
[6]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural network 2015. arXiv preprint arXiv: 150503540.
[7]
Silveira M, Nascimento JC, Marques JS, et al. Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J Sel Top Signal Process 2009; 3(1): 35-45.
[http://dx.doi.org/10.1109/JSTSP.2008.2011119]
[8]
Zhennan Yan , Yiqiang Zhan , Zhigang Peng , et al. Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition. IEEE Trans Med Imaging 2016; 35(5): 1332-43.
[http://dx.doi.org/10.1109/TMI.2016.2524985] [PMID: 26863652]
[9]
Shun Miao , Wang ZJ, Rui Liao . A CNN regression approach for real-time 2D/3D registration. IEEE Trans Med Imaging 2016; 35(5): 1352-63.
[http://dx.doi.org/10.1109/TMI.2016.2521800] [PMID: 26829785]
[10]
Campos-do-Carmo G, Ramos-e-Silva M. Dermoscopy: basic concepts. Int J Dermatol 2008; 47(7): 712-9.
[http://dx.doi.org/10.1111/j.1365-4632.2008.03556.x] [PMID: 18613881]
[11]
Clawson KM, Scotney B, Morrow PJ, et al. Determination of optimal axes for skin lesion asymmetry quantification. In: International Conference on Image Processing. San Antonio, TX, USA, New Jersey: IEEE 2007; pp. 453-6.
[http://dx.doi.org/10.1109/ICIP.2007.4379190]
[12]
Ng VT, Fung BY, Lee TK. Determining the asymmetry of skin lesion with fuzzy borders. Comput Biol Med 2005; 35(2): 103-20.
[http://dx.doi.org/10.1016/j.compbiomed.2003.11.004] [PMID: 15567181]
[13]
Claridge E, Hall PN, Keefe M, Allen JP. Shape analysis for classification of malignant melanoma. J Biomed Eng 1992; 14(3): 229-34.
[http://dx.doi.org/10.1016/0141-5425(92)90057-R] [PMID: 1588780]
[14]
Green A, Martin N, Pfitzner J, O’Rourke M, Knight N. Computer image analysis in the diagnosis of melanoma. J Am Acad Dermatol 1994; 31(6): 958-64.
[http://dx.doi.org/10.1016/S0190-9622(94)70264-0] [PMID: 7962777]
[15]
Khan A, Gupta K, Stanley RJ, et al. Fuzzy logic techniques for blotch feature evaluation in dermoscopy images. Comput Med Imaging Graph 2009; 33(1): 50-7.
[http://dx.doi.org/10.1016/j.compmedimag.2008.10.001] [PMID: 19027266]
[16]
Krzysztof JP. The analysis of skin lesions asymmetry, border irregularity and colour changes in the process of malignant melanoma autonomous recognition in dermatoscopic images. Diss Instytut Informatyki 2014; 2014: 1-10.
[17]
Fleming MG, Steger C, Zhang J, et al. Techniques for a structural analysis of dermatoscopic imagery. Comput Med Imaging Graph 1998; 22(5): 375-89.
[http://dx.doi.org/10.1016/S0895-6111(98)00048-2] [PMID: 9890182]
[18]
Sadeghi M, Razmara M, Lee TK, Atkins MS. A novel method for detection of pigment network in dermoscopic images using graphs. Comput Med Imaging Graph 2011; 35(2): 137-43.
[http://dx.doi.org/10.1016/j.compmedimag.2010.07.002] [PMID: 20724109]
[19]
Anantha M, Moss RH, Stoecker WV. Detection of pigment network in dermatoscopy images using texture analysis. Comput Med Imaging Graph 2004; 28(5): 225-34.
[http://dx.doi.org/10.1016/j.compmedimag.2004.04.002] [PMID: 15249068]
[20]
Barata C, Marques JS, Rozeira J. A system for the detection of pigment network in dermoscopy images using directional filters. IEEE Trans Biomed Eng 2012; 59(10): 2744-54.
[http://dx.doi.org/10.1109/TBME.2012.2209423] [PMID: 22829364]
[21]
Jaworek-Korjakowska J, Tadeusiewicz R. Assessment of dots and globules in dermoscopic color images as one of the 7-point check list criteria. In: 20th International Conference on Image Processing. Melbourne, Australia, New Jersey: IEEE 2013; pp. 1456-60.
[http://dx.doi.org/10.1109/ICIP.2013.6738299]
[22]
Braun RP, Gaide O, Oliviero M, et al. The significance of multiple blue-grey dots (granularity) for the dermoscopic diagnosis of melanoma. Br J Dermatol 2007; 157(5): 907-13.
[http://dx.doi.org/10.1111/j.1365-2133.2007.08145.x] [PMID: 17725673]
[23]
Balch CM, Gershenwald JE, Soong S, et al. Final version of 2009 AJCC melanoma staging and classification. J Clin Oncol 2009; 27(36): 6199-206.
[24]
Sadeghi M, Lee TK, McLean D, Lui H, Atkins MS. Detection and analysis of irregular streaks in dermoscopic images of skin lesions. IEEE Trans Med Imaging 2013; 32(5): 849-61.
[http://dx.doi.org/10.1109/TMI.2013.2239307] [PMID: 23335664]
[25]
Menzies SW, Ingvar C, McCarthy WH. A sensitivity and specificity analysis of the surface microscopy features of invasive melanoma. Melanoma Res 1996; 6(1): 55-62.
[http://dx.doi.org/10.1097/00008390-199602000-00008] [PMID: 8640071]
[26]
Steiner A, Pehamberger H, Binder M, Wolff K. Pigmented Spitz nevi: improvement of the diagnostic accuracy by epiluminescence microscopy. J Am Acad Dermatol 1992; 27(5 Pt 1): 697-701.
[http://dx.doi.org/10.1016/0190-9622(92)70240-G] [PMID: 1430390]
[27]
Pehamberger H, Steiner A, Wolff K. In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions. J Am Acad Dermatol 1987; 17(4): 571-83.
[http://dx.doi.org/10.1016/S0190-9622(87)70239-4] [PMID: 3668002]
[28]
Fleming MG, Steger C, Zhang J, et al. Techniques for a structural analysis of dermatoscopic imagery. Comput Med Imaging Graph 1998; 22(5): 375-89.
[http://dx.doi.org/10.1016/S0895-6111(98)00048-2] [PMID: 9890182]
[29]
Betta G, Di Leo Ge. Automated application of the 7-point checklist diagnosis method for skin lesions: Estimation of chromatic and shape parameters. In: Instrumentation and Measurement Technology Conference Proceedings. Ottawa, Ont., Canada, New Jersey: IEEE 2006; pp. 1818-22.
[http://dx.doi.org/10.1109/IMTC.2005.1604486]
[30]
Mirzaalian H, Lee TK, Hamarneh G. Learning features for streak detection in dermoscopic color images using localized radial flux of principal intensity curvature. In: Workshop on Mathematical Methods in Biomedical Image Analysis. Breckenridge, CO, USA, New Jersey: IEEE 2012; pp. 97-101.
[http://dx.doi.org/10.1109/MMBIA.2012.6164758]
[31]
Sadeghi M, Lee TK, McLean D, Lui H, Atkins MS. Detection and analysis of irregular streaks in dermoscopic images of skin lesions. IEEE Trans Med Imaging 2013; 32(5): 849-61.
[http://dx.doi.org/10.1109/TMI.2013.2239307] [PMID: 23335664]
[32]
Celebi ME, Iyatomi H, Stoecker WV, et al. Automatic detection of blue-white veil and related structures in dermoscopy images. Comput Med Imaging Graph 2008; 32(8): 670-7.
[http://dx.doi.org/10.1016/j.compmedimag.2008.08.003] [PMID: 18804955]
[33]
Arroyo JLG, Zapirain BG, Zorrilla AM. Blue-white veil and dark-red patch of pigment pattern recognition in dermoscopic images using machine-learning techniques. In: International Symposium on Signal Processing and Information Technology (ISSPIT). Bilbao, Spain, New Jersey: IEEE 2011; pp. 196-201.
[http://dx.doi.org/10.1109/ISSPIT.2011.6151559]
[34]
Silveira M, Nascimento JC, Marques JS, et al. Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images. IEEE J Sel Top Signal Process 2009; 3(1): 35-45.
[http://dx.doi.org/10.1109/JSTSP.2008.2011119]
[35]
Poynton C. Digital Video and HD: Algorithms and Interfaces. 2nd ed. Morgan Kaufmann 2012.
[36]
Celebi ME, Iyatomi H, Schaefer G. Contrast enhancement in dermoscopy images by maximizing a histogram bimodality measure. In: Proceedings of the 16th IEEE International Conference on Image Processing. Cairo, Egypt, New Jersey, IEEE. 2601-4.
[http://dx.doi.org/10.1109/ICIP.2009.5413990]
[37]
Madooei A, Drew MS, Sadeghi M, Atkins MS. Automated pre-processing method for dermoscopic images and its application to pigmented skin lesion segmentation. In: Proceedings of the 20th Color and Imaging Conference: Color Science and EngineeringSystems, Technologies, and Applications. Los Angeles, California, USA. Springfield: SIST 2012; pp. 158-63.
[38]
Pratt WK. Spatial Transform coding of color images. IEEE Trans Commun Technol 1971; 19(6): 980-92.
[http://dx.doi.org/10.1109/TCOM.1971.1090769]
[39]
Gao J, Zhang J, Fleming MG, Pollak I, Cognetta AB. Segmentation of dermatoscopic images by stabilized inverse diffusion equations. In: Proceedings of the 1998 International Conference on Image Processing. Chicago, IL, USA, USA, New Jersey: IEEE 2002; pp. 823-7.
[40]
Gevers T, Smeulders AWM. Color-based object recognition. Pattern Recognit 1999; 32(3): 453-64.
[http://dx.doi.org/10.1016/S0031-3203(98)00036-3]
[41]
Busin L, Vandenbroucke N, Macaire L. In: Advances in Imaging and Electron Physics. Cambridge: Academic Press 2008; pp. 65-168.
[42]
Vander HY, Naeyaert JMAD, Lemahieu I, Philips W. An imaging system with calibrated color image acquisition for use in dermatology. IEEE Trans Med Imaging 2000; 19(7): 722-30.
[http://dx.doi.org/10.1109/42.875195] [PMID: 11055787]
[43]
Grana C, Pellacani G, Seidenari S. Practical color calibration for dermoscopy, applied to a digital epiluminescence microscope. Skin Res Technol 2005; 11(4): 242-7.
[http://dx.doi.org/10.1111/j.0909-725X.2005.00127.x] [PMID: 16221140]
[44]
Møllersen K, Kirchesch HM, Schopf TG, Godtliebsen F. Unsupervised segmentation for digital dermoscopic images. Skin Res Technol 2010; 16(4): 401-7.
[http://dx.doi.org/10.1111/j.1600-0846.2010.00455.x] [PMID: 20923456]
[45]
Quintana J, Garcia R, Neumann L. A novel method for color correction in epiluminescence microscopy. Comput Med Imaging Graph 2011; 35(7-8): 646-52.
[http://dx.doi.org/10.1016/j.compmedimag.2011.03.006] [PMID: 21531539]
[46]
Wighton P, Lee TK, Lui H, McLean D, Atkins MS. Chromatic aberration correction: an enhancement to the calibration of low-cost digital dermoscopes. Skin Res Technol 2011; 17(3): 339-47.
[http://dx.doi.org/10.1111/j.1600-0846.2011.00504.x] [PMID: 21338405]
[47]
Delalleau A, Lagarde JM, George J. An a priori shading correction technique for contact imaging devices. IEEE Trans Image Process 2011; 20(10): 2876-85.
[http://dx.doi.org/10.1109/TIP.2011.2142003] [PMID: 21507774]
[48]
Iyatomi H, Oka H, Celebi ME, et al. An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Comput Med Imaging Graph 2008; 32(7): 566-79.
[http://dx.doi.org/10.1016/j.compmedimag.2008.06.005] [PMID: 18703311]
[49]
Gómez DD, Butakoff C, Ersbøll BK, Stoecker W. Independent histogram pursuit for segmentation of skin lesions. IEEE Trans Biomed Eng 2008; 55(1): 157-61.
[http://dx.doi.org/10.1109/TBME.2007.910651] [PMID: 18232357]
[50]
Otsu N. A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern 1979; 9(1): 62-6.
[http://dx.doi.org/10.1109/TSMC.1979.4310076]
[51]
Iyatomi H, Celebi ME, Schaefer G, Tanaka M. Automated color calibration method for dermoscopy images. Comput Med Imaging Graph 2011; 35(2): 89-98.
[http://dx.doi.org/10.1016/j.compmedimag.2010.08.003] [PMID: 20933366]
[52]
Schaefer G, Rajab MI, Celebi ME, Iyatomi H. Colour and contrast enhancement for improved skin lesion segmentation. Comput Med Imaging Graph 2011; 35(2): 99-104.
[http://dx.doi.org/10.1016/j.compmedimag.2010.08.004] [PMID: 21035303]
[53]
Rizzi A, Gatta C, Marini D. A new algorithm for unsupervised global and local color correction. Pattern Recognit Lett 2003; 24(11): 1663-77.
[http://dx.doi.org/10.1016/S0167-8655(02)00323-9]
[54]
Abbas Q, Fondón I, Rashid M. Unsupervised skin lesions border detection via two-dimensional image analysis. Comput Methods Prog Biomed 2011; 104(3): e1-15.
[http://dx.doi.org/10.1016/j.cmpb.2010.06.016] [PMID: 20663582]
[55]
Abbas Q, Celebi ME, Fondón García I, Rashid M. Lesion border detection in dermoscopy images using dynamic programming. Skin Res Technol 2011; 17(1): 91-100.
[http://dx.doi.org/10.1111/j.1600-0846.2010.00472.x] [PMID: 21226876]
[56]
Norton KA, Iyatomi H, Celebi ME, et al. Three-phase general border detection method for dermoscopy images using non-uniform illumination correction. Skin Res Technol 2012; 18(3): 290-300.
[http://dx.doi.org/10.1111/j.1600-0846.2011.00569.x] [PMID: 22092500]
[57]
Stockham TG. Image processing in the context of a visual model. Proc IEEE 1972; 60(7): 828-42.
[http://dx.doi.org/10.1109/PROC.1972.8782]
[58]
Pizer SM, Amburn EP, Austin JD, et al. Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 1987; 39(3): 355-68.
[http://dx.doi.org/10.1016/S0734-189X(87)80186-X]
[59]
Barata C, Celebi ME, Marques JS. Improving dermoscopy image classification using color constancy. IEEE J Biomed Health Inform 2015; 19(3): 1146-52.
[PMID: 25073179]
[60]
Lu Y, Xie F, Wu Y, Jiang Z, Meng R. No reference uneven illumination assessment for dermoscopy images. IEEE Signal Process Lett 2015; 22(5): 534-8.
[http://dx.doi.org/10.1109/LSP.2014.2357015]
[61]
Kimmel R, Elad M, Shaked D, Keshet R, Sobel I. A variational framework for retinex. Int J Comput Vis 2003; 52(1): 7-23.
[http://dx.doi.org/10.1023/A:1022314423998]
[62]
Celebi ME, Kingravi HA, Aslandogan YA. Nonlinear vector filtering for impulsive noise removal from color images. J Electron Imaging 2007; 16(3): 1-21.
[63]
Schmid P. Segmentation of digitized dermatoscopic images by two-dimensional color clustering. IEEE Trans Med Imaging 1999; 18(2): 164-71.
[http://dx.doi.org/10.1109/42.759124] [PMID: 10232673]
[64]
Geusebroek JM, Smeulders AWM, van de Weijer J. Fast anisotropic Gauss filtering. IEEE Trans Image Process 2003; 12(8): 938-43.
[http://dx.doi.org/10.1109/TIP.2003.812429] [PMID: 18237967]
[65]
Nakariyakul S. Fast spatial averaging: An efficient algorithm for 2D Mean Filtering. J Supercomput 2013; 65(1): 262-73.
[http://dx.doi.org/10.1007/s11227-011-0638-9]
[66]
Perreault S, Hébert P. Median filtering in constant time. IEEE Trans Image Process 2007; 16(9): 2389-94.
[http://dx.doi.org/10.1109/TIP.2007.902329] [PMID: 17784612]
[67]
Abbas Q, Celebi ME, Fondón García I. Skin tumor area extraction using an improved dynamic programming approach. Skin Res Technol 2012; 18(2): 133-42.
[http://dx.doi.org/10.1111/j.1600-0846.2011.00544.x] [PMID: 21507072]
[68]
Soille P. Morphological image analysis: Principles and applications. 2nd ed. Berlin: Springer 2004.
[http://dx.doi.org/10.1007/978-3-662-05088-0]
[69]
Lee T, Ng V, Gallagher R, Coldman A, McLean D. DullRazor: a software approach to hair removal from images. Comput Biol Med 1997; 27(6): 533-43.
[http://dx.doi.org/10.1016/S0010-4825(97)00020-6] [PMID: 9437554]
[70]
Zhou H, Chen M, Gass R, et al. Feature-Preserving Artifact Removal from Dermoscopy Images in Proceedings of the SPIE Medical Imaging 2008 Conference. San Diego, CA, USA. Bellingham: SPIE. 2008; pp. 1-9.
[http://dx.doi.org/10.1117/12.770824]
[71]
Wighton P, Lee TK, Atkins MS. Dermascopic hair disocclusion using inpainting.Proceedings of the SPIE Medical Imaging 2008 Conference. San Diego, CA, USA. Bellingham: SPIE 2008; pp. 691427-1.
[http://dx.doi.org/10.1117/12.770776]
[72]
Fiorese M, Peserico E, Silletti A. Virtual shave: Automated hair removal from digital dermatoscopic images. In: Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Boston, MA, USA, New Jersey: IEEE 2011; pp. 5145-8.
[http://dx.doi.org/10.1109/IEMBS.2011.6091274]
[73]
Schmid-Saugeona P, Guillodb J, Thirana JP. Towards a computer-aided diagnosis system for pigmented skin lesions. Comput Med Imaging Graph 2003; 27(1): 65-78.
[http://dx.doi.org/10.1016/S0895-6111(02)00048-4] [PMID: 12573891]
[74]
Xie FY, Qin SY, Jiang ZG, Meng RS. PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma. Comput Med Imaging Graph 2009; 33(4): 275-82.
[http://dx.doi.org/10.1016/j.compmedimag.2009.01.003] [PMID: 19261439]
[75]
Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990; 12(7): 629-39.
[http://dx.doi.org/10.1109/34.56205]
[76]
Bertalmio M, Sapiro G, Caselles V, Ballester C. Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques. New York, NY, USA, New York: ACM 2000; pp. 417-24.
[77]
Steger C. An unbiased detector of curvilinear structures. IEEE Trans Pattern Anal Mach Intell 1998; 20(2): 113-25.
[http://dx.doi.org/10.1109/34.659930]
[78]
Criminisi A, Pérez P, Toyama K. Region filling and object removal by exemplar-based image inpainting. IEEE Trans Image Process 2004; 13(9): 1200-12.
[http://dx.doi.org/10.1109/TIP.2004.833105] [PMID: 15449582]
[79]
Nguyen NH, Lee TK, Atkins MS. Segmentation of light and dark hair in dermoscopic images: a hybrid approach using a universal kernel. In: Proceedings of the SPIE Medical Imaging 2010 Conference. San Diego, CA, USA. Bellingham: SPIE 2010; pp. 76234N-1.
[http://dx.doi.org/10.1117/12.844572]
[80]
Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 1989; 8(3): 263-9.
[http://dx.doi.org/10.1109/42.34715] [PMID: 18230524]
[81]
Pal NR, Pal SK. Entropic thresholding. Sign Process 1989; 16(2): 97-108.
[http://dx.doi.org/10.1016/0165-1684(89)90090-X]
[82]
Li Q, Zhang L, You J, Zhang D, Bhattacharya P. Dark line detection with line width extraction. In: Proceedings of the 15th IEEE International Conference on Image Processing. San Diego, CA, USA, New Jersey: IEEE 2008; pp. 621-4.
[http://dx.doi.org/10.1109/ICIP.2008.4711831]
[83]
Kiani K, Sharafat AR. E-shaver: an improved DullRazor(®) for digitally removing dark and light-colored hairs in dermoscopic images. Comput Biol Med 2011; 41(3): 139-45.
[http://dx.doi.org/10.1016/j.compbiomed.2011.01.003] [PMID: 21316042]
[84]
Jafari-Khouzani K, Soltanian-Zadeh H Sr. Radon transform orientation estimation for rotation invariant texture analysis. IEEE Trans Pattern Anal Mach Intell 2005; 27(6): 1004-8.
[http://dx.doi.org/10.1109/TPAMI.2005.126] [PMID: 15945146]
[85]
Prewitt JMS. Object Enhancement and Extraction. In: Picture Processing and Psychopictorics. Cambridge: Academic Press 1970.
[86]
Fukunaga K. Introduction to Statistical Pattern Recognition. 2nd ed. Cambridge: Academic Press 1990.
[87]
Kopparapu SK, Desai UB. Bayesian approach to image interpretation. Berlin: Kluwer Academic Publishers 2001.
[88]
Rue H, Held L. Gaussian markov random fields: Theory and applications. Chapman and Hall/CRC 2005.
[http://dx.doi.org/10.1201/9780203492024]
[89]
Afonso A, Silveira M. Hair Detection in dermoscopic images using percolation.Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego, CA, USA, New Jersey: IEEE 2012; pp. 4378-81.
[http://dx.doi.org/10.1109/EMBC.2012.6346936]
[90]
Yamaguchi T, Nakamura S, Hashimoto S. An efficient crack detection method using percolation based image. In: 3rd IEEE Conference on Industrial Electronics and Applications. Singapore. New Jersey: IEEE 1875-80.
[91]
Abbas Q, Garcia IF, Emre Celebi M, Ahmad W. A feature-preserving hair removal algorithm for dermoscopy images. Skin Res Technol 2013; 19(1): e27-36.
[http://dx.doi.org/10.1111/j.1600-0846.2011.00603.x] [PMID: 22211360]
[92]
Zhang B, Zhang L, Zhang L, Karray F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 2010; 40(4): 438-45.
[http://dx.doi.org/10.1016/j.compbiomed.2010.02.008] [PMID: 20202631]
[93]
Bornemann F, Marz T. Fast Image Inpainting Based on Coherence Transport. J Math Imaging Vis 2007; 28(3): 259-78.
[http://dx.doi.org/10.1007/s10851-007-0017-6]
[94]
Toossi MTB, Pourreza HR, Zare H, Sigari MH, Layegh P, Azimi A. An effective hair removal algorithm for dermoscopy images. Skin Res Technol 2013; 19(3): 230-5.
[http://dx.doi.org/10.1111/srt.12015] [PMID: 23560826]
[95]
Pratt WK. Generalized wiener filtering computation techniques. IEEE Trans Comput 1972; C21(7): 636-41.
[http://dx.doi.org/10.1109/T-C.1972.223567]
[96]
Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; 8(6): 679-98.
[http://dx.doi.org/10.1109/TPAMI.1986.4767851] [PMID: 21869365]
[97]
Rosenfeld A, de la Torre P. Histogram concavity analysis as an aid in threshold selection. IEEE Trans Syst Man Cybern 1983; 13(2): 231-5.
[http://dx.doi.org/10.1109/TSMC.1983.6313118]
[98]
Abbas Q, Celebi ME, Garcia IF. Hair removal methods: A comparative study for dermoscopy images. Biomed Signal Process Control 2011; 6(4): 395-404.
[http://dx.doi.org/10.1016/j.bspc.2011.01.003]
[99]
Mirzaalian H, Lee TK, Hamarneh G. Hair enhancement in dermoscopic images using dual-channel quaternion tubularness filters and MRF-based multilabel optimization. IEEE Trans Image Process 2014; 23(12): 5486-96.
[http://dx.doi.org/10.1109/TIP.2014.2362054] [PMID: 25312927]
[100]
Frangi AF, Niessen WJ, Vincken KL, Viergever MA. Multiscale vessel enhancement filtering. In: Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin: Springer 2006; pp. 130-7.
[101]
Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 2001; 23(11): 1222-39.
[http://dx.doi.org/10.1109/34.969114]
[102]
Celebi ME, Iyatomi H, Schaefer G, Stoecker WV. Approximate lesion localization in dermoscopy images. Skin Res Technol 2009; 15(3): 314-22.
[http://dx.doi.org/10.1111/j.1600-0846.2009.00357.x] [PMID: 19624428]
[103]
Wang H, Chen X, Moss RH, et al. Watershed segmentation of dermoscopy images using a watershed technique. Skin Res Technol 2010; 16(3): 378-84.
[http://dx.doi.org/10.1111/j.1600-0846.2010.00445.x] [PMID: 20637008]
[104]
Wang H, Moss RH, Chen X, et al. Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images. Comput Med Imaging Graph 2011; 35(2): 116-20.
[http://dx.doi.org/10.1016/j.compmedimag.2010.09.006] [PMID: 20970307]
[105]
Erkol B, Moss RH, Stanley RJ, Stoecker WV, Hvatum E. Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Res Technol 2005; 11(1): 17-26.
[http://dx.doi.org/10.1111/j.1600-0846.2005.00092.x] [PMID: 15691255]
[106]
Abbas Q, Celebi ME, Garcia IF. A novel perceptually-oriented approach for skin tumor segmentation. Int J Innov Comput, Inf Control 2012; 8(3): 1837-48.
[107]
Emre Celebi M, Alp Aslandogan Y, Stoecker WV, Iyatomi H, Oka H, Chen X. Unsupervised border detection in dermoscopy images. Skin Res Technol 2007; 13(4): 454-62.
[http://dx.doi.org/10.1111/j.1600-0846.2007.00251.x] [PMID: 17908199]
[108]
Celebi ME, Kingravi HA, Iyatomi H, et al. Border detection in dermoscopy images using statistical region merging. Skin Res Technol 2008; 14(3): 347-53.
[http://dx.doi.org/10.1111/j.1600-0846.2008.00301.x] [PMID: 19159382]
[109]
Kittler J, Illingworth J. Minimum error thresholding. Pattern Recognit 1986; 19(1): 41-7.
[http://dx.doi.org/10.1016/0031-3203(86)90030-0]
[110]
Forsythe GE. Generation and use of orthogonal polynomials for data-fitting with a digital computer. J Soc Ind Appl Math 1957; 5(2): 74-88.
[http://dx.doi.org/10.1137/0105007]
[111]
Sonka M, Hlavac V, Boyle R. Image processing, analysis, and machine vision. 4th ed. Cengage Learning 2014.
[112]
Peruch F, Bogo F, Bonazza M, Cappelleri VM, Peserico E. Simpler, faster, more accurate melanocytic lesion segmentation through MEDS. IEEE Trans Biomed Eng 2014; 61(2): 557-65.
[http://dx.doi.org/10.1109/TBME.2013.2283803] [PMID: 24081839]
[113]
Mendonca T, Marcal ARS, Vieira A, et al. Comparison of segmentation methods for automatic diagnosis of dermoscopy images. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology. Lyon, France, New Jersey: IEEE 2007; pp. 6572-5.
[http://dx.doi.org/10.1109/IEMBS.2007.4353865]
[114]
Haeghen YV, Naeyaert JM, Lemahieu I. Development of a dermatological workstation: preliminary results on lesion segmentation in CIE L*A*B* color space.Proceedings of the International Conference on Color in Graphics and Image Processing. Saint-Štienne, France 2000.
[115]
Schmid P. Lesion detection in dermatoscopic images using anisotropic diffusion and morphological flooding. In: Proceedings of the IEEE ICIP 1999 Conference. Kobe, Japan. New Jersey: IEEE. 449-53.
[http://dx.doi.org/10.1109/ICIP.1999.817154]
[116]
Donadey T, Serruys C, Giron A. Boundary detection of black skin tumors using an adaptive radial-based approach. In: Proceedings of the SPIE Medical Imaging 2000 Conference. San Diego, CA, USA. Bellingham: SPIE 2000; pp. 810-6.
[http://dx.doi.org/10.1117/12.387744]
[117]
Gao J, Zhang J, Fleming MG. Segmentation of dermatoscopic images by stabilized inverse diffusion equations. In: Proceedings of the IEEE ICIP 1998 Conference. Chicago, IL, USA. New Jersey: IEEE. 823-7.
[118]
Celebi ME, Kingravi HA, Iyatomi H, et al. Border detection in dermoscopy images using statistical region merging. Skin Res Technol 2008; 14(3): 347-53.
[http://dx.doi.org/10.1111/j.1600-0846.2008.00301.x] [PMID: 19159382]
[119]
LeCun Y, Bottou L, Bengio Y. Gradient-based learning applied to document recognition. Proceed IEEE 1998; 86(11): 2278-324.
[120]
Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 2012; 2012: 1097-105.
[121]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition 2014. arXiv:14091556
[122]
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions 2014. arXiv:1409484.
[123]
Lin M, Chen Q, Yan S. Network in network 2013. arXiv: 13124400.
[124]
He K, Zhang X, Ren S, Sun J. Deep residual learning for Image recognition 2015. arXiv:151203385.
[125]
Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, New Jersey: IEEE 2016; pp. 2818-826.
[http://dx.doi.org/10.1109/CVPR.2016.308]
[126]
Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning. AAAI 2017; 4: 4278-84.
[127]
Peyman S. GholamHosseini H. Lesion border detection using deep learning. In: Congress on Evolutionary Computation (CEC); Vancouver, BC, Canada. New Jersey: IEEE 2016; pp. 1416-21.
[128]
Jafari MH, et al. Skin lesion segmentation in clinical images using deep learning. In: 23rd International Conference on Pattern Recognition (ICPR). Cancun, Mexico, New Jersey: IEEE 2016; pp. 337-42.
[http://dx.doi.org/10.1109/ICPR.2016.7899656]
[129]
Yuan Y, Chao M, Lo Y-C. Automatic skin lesion segmentation using deep fully convolutional networks with Jaccard distance. IEEE Trans Med Imaging 2017; 36(9): 1876-86.
[http://dx.doi.org/10.1109/TMI.2017.2695227] [PMID: 28436853]
[130]
Bi L, Kim J, Ahn E, Kumar A, Fulham M, Feng D. Dermoscopic image segmentation via multistage fully convolutional networks. IEEE Trans Biomed Eng 2017; 64(9): 2065-74.
[http://dx.doi.org/10.1109/TBME.2017.2712771] [PMID: 28600236]
[131]
Bi L, Kim J, Ahn E, et al. Semi-automatic skin lesion segmentation via fully convolutional networks. In: 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, Australia, New Jersey: IEEE 2017; pp. 561-4.
[http://dx.doi.org/10.1109/ISBI.2017.7950583]
[132]
Bozorgtabar B, Ge Z, Chakravorty R, et al. Investigating deep side layers for skin lesion segmentation. In: 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, Australia, New Jersey: IEEE 2017; pp. 256-60.
[http://dx.doi.org/10.1109/ISBI.2017.7950514]
[133]
Attia M, Hossny M, Nahavandi S, et al. Skin melanoma segmentation using recurrent and convolutional neural networks. In: 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, Australia, New Jersey: IEEE 2017; pp. 292-6.
[http://dx.doi.org/10.1109/ISBI.2017.7950522]
[134]
Gutman D. Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). 2016. Available from:. https://arxiv.org/abs/1605.01397
[135]
Yu L, Chen H, Dou Q, Qin J, Heng PA. Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging 2017; 36(4): 994-1004.
[http://dx.doi.org/10.1109/TMI.2016.2642839] [PMID: 28026754]
[136]
Celebi ME, Kingravi HA, Iyatomi H, et al. Border detection in dermoscopy images using statistical region merging. Skin Res Technol 2008; 14(3): 347-53.
[137]
Cavalcanti PG, Yari Y, Scharcanski J. Pigmented skin lesion segmentation on macroscopic images. In: 25th International Conference of Image and Vision Computing New Zealand. Queenstown, New Zealand, New Jersey: IEEE 2010; pp. 1-7.
[http://dx.doi.org/10.1109/IVCNZ.2010.6148845]
[138]
Cavalcanti Pablo G, Scharcanski Jacob. Shading attenuation in human skin color images. In: International Symposium on Visual Computing. Berlin, Heidelberg: Springer 2010; pp. 190-8.
[139]
Cavalcanti PG, Scharcanski J. Automated prescreening of pigmented skin lesions using standard cameras. Comput Med Imaging Graph 2011; 35(6): 481-91.
[http://dx.doi.org/10.1016/j.compmedimag.2011.02.007] [PMID: 21489751]
[140]
Glaister J, Wong A, Clausi DA. Segmentation of skin lesions from digital images using joint statistical texture distinctiveness. IEEE Trans Biomed Eng 2014; 61(4): 1220-30.
[http://dx.doi.org/10.1109/TBME.2013.2297622] [PMID: 24658246]
[141]
Shih TY. The reversibility of six geometric color spaces. Photogramm Eng Remote Sensing 1995; 61(10): 1223-32.
[142]
Ballerini L, Fisher RB, Aldridge B, Rees J. A color and texture based hierarchical k-NN approach to the classification of non-melanoma skin lesions. In: Color Medical Image Analysis. Berlin, Heidelberg: Springer 2013; pp. 63-86.
[143]
Glowacz A, Glowacz Z. Recognition of images of finger skin with application of histogram, image filtration and k-NN classifier. Biocybern Biomed Eng 2016; 36(1): 95-101.
[http://dx.doi.org/10.1016/j.bbe.2015.12.005]
[144]
Premaladha J, Ravichandran KS. Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms. J Med Syst 2016; 40(4): 96.
[http://dx.doi.org/10.1007/s10916-016-0460-2] [PMID: 26872778]
[145]
Beuren AT, Pinheiro RJG, Facon J. Color approach of melanoma lesion segmentation. In: Systems, Signals and Image Processing. New Jersey: IEEE 2012; pp. 284-7.
[146]
Gilmore S, Hofmann-Wellenhof R, Soyer HP. A support vector machine for decision support in melanoma recognition. Exp Dermatol 2010; 19(9): 830-5.
[http://dx.doi.org/10.1111/j.1600-0625.2010.01112.x] [PMID: 20629732]
[147]
Majtner T, Stoklasa R, Svoboda D. RSURF-the efficient texture-based descriptor for fluorescence microscopy images of HEp-2 Cells. In: 22nd International Conference on Pattern Recognition. Stockholm, Sweden; New Jersey: IEEE 2014; pp. 1194-9.
[http://dx.doi.org/10.1109/ICPR.2014.215]
[148]
Riaz F, Hassan A, Javed MY, Coimbra MT. Detecting melanoma in dermoscopy images using scale adaptive local binary patterns. Conf Proc IEEE Eng Med Biol Soc 2014; 2014: 6758-61.
[149]
Majtner T, Yildirim-Yayilgan S, Hardeberg JY. Combining deep learning and hand-crafted features for skin lesion classification. In: 6th International Conference on Image Processing Theory Tools and Applications (IPTA). Oulu, Finland, New Jersey: IEEE 2017; pp. 1-6.
[http://dx.doi.org/10.1109/IPTA.2016.7821017]
[150]
Nasr-Esfahani E, Samavi S, Karimi N, et al. Melanoma detection by analysis of clinical images using convolutional neural network. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando, FL, USA, New Jersey: IEEE 2016; pp. 1373-76.
[http://dx.doi.org/10.1109/EMBC.2016.7590963]
[151]
Mahdiraji SA, Baleghi Y, Sakhaei SM. Skin lesion images classification using new color pigmented boundary descriptors. In: 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA). Shahrekord, Iran, New Jersey: IEEE 2017; pp. 102-7.
[http://dx.doi.org/10.1109/PRIA.2017.7983026]
[152]
Li Y, Shen L. Skin lesion analysis towards melanoma detection using deep learning network. Sensors (Basel) 2018; 18(2): 556.
[http://dx.doi.org/10.3390/s18020556] [PMID: 29439500]
[153]
Lopez AR, Giro-i-Nieto X, Burdick J, Marques O. Skin lesion classification from dermoscopic images using deep learning techniques. In: 13th IASTED International Conference on Biomedical Engineering (BioMed). Innsbruck, Austria, New Jersey: IEEE 2017; pp. 49-54.
[154]
Codella NC, Nguyen QB, Pankanti S, et al. Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J Res Develop 2017; 61(4): 5-1.
[http://dx.doi.org/10.1147/JRD.2017.2708299]
[155]
Dermofit Image library. Available from:. https://licensing.eri.ed.ac.uk/i/software/dermofit image-library.html
[156]
International Skin Imaging Collaboration: Melanoma Project Website Available from:. https://isic-archive. com/
[157]
PH2 Database. Available from:. https://www.fc.up.pt/addi/ph2%20database.html
[158]
Dermatology Database used in MED-NODE Available from:. http://www.cs.rug.nl/~imaging/databases/melanoma_naevi/

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