Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

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

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Computer-aided Diagnosis of Skin Cancer: A Review

Author(s): Navid Razmjooy*, Mohsen Ashourian, Maryam Karimifard, Vania V. Estrela, Hermes J. Loschi, Douglas do Nascimento, Reinaldo P. França and Mikhail Vishnevski

Volume 16, Issue 7, 2020

Page: [781 - 793] Pages: 13

DOI: 10.2174/1573405616666200129095242

Price: $65

Abstract

Cancer is currently one of the main health issues in the world. Among different varieties of cancers, skin cancer is the most common cancer in the world and accounts for 75% of the world's cancer. Indeed, skin cancer involves abnormal changes in the outer layer of the skin. Although most people with skin cancer recover, it is one of the major concerns of people due to its high prevalence. Most types of skin cancers grow only locally and invade adjacent tissues, but some of them, especially melanoma (cancer of the pigment cells), which is the rarest type of skin cancer, may spread through the circulatory system or lymphatic system and reach the farthest points of the body. Many papers have been reviewed about the application of image processing in cancer detection. In this paper, the automatic skin cancer detection and also different steps of such a process have been discussed based on the implantation capabilities.

Keywords: Computer-aided diagnosis, image processing, segmentation, skin cancer, lesions, melanoma.

Graphical Abstract

[1]
Queen L. Skin cancer: causes, prevention, and treatment 2017 Available from: https://digitalcommons.liberty.edu/honors/648/
[2]
Kaur P, Singh G, Kaur P. A review of denoising medical images using machine learning approaches. Curr Med Imaging Rev 2018; 14(5): 675-85.
[http://dx.doi.org/10.2174/1573405613666170428154156] [PMID: 30532667]
[3]
Park J, Song DH, Han S-S, Joo Lee S, Baek Kim K. Automatic extraction of soft tissue tumor from ultrasonography using ART2 based intelligent image analysis. Curr Med Imaging Rev 2017; 13(4): 447-53.
[http://dx.doi.org/10.2174/1573405613666170504153002]
[4]
Hamel R, Dejarnac O, Wichit S, et al. Biology of Zika virus infection in human skin cells. J Virology 2015; 89(17): 8880-96.
[5]
Estrela VV, Herrmann AE. Content-Based Image Retrieval (CBIR) in remote clinical diagnosis and healthcare encyclopedia of e-health and telemedicine. IGI Global 2016 2016; 495-520.
[6]
Rajinikanth V, Satapathy SC, Dey N, Fernandes SL, Manic KS. Skin melanoma assessment using kapur’s entropy and level set-a study with bat algorithm. Smart Int Comp Appl 2019; 2019: 193-202.
[7]
Rajinikanth V, Madhavaraja N, Satapathy SC, Fernandes SL. Otsu’s multi-thresholding and active contour snake model to segment dermoscopy images. J Med Imaging Health Inform 2017; 7(8): 1837-40.
[http://dx.doi.org/10.1166/jmihi.2017.2265]
[8]
Kasmi R, Mokrani K. Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule. IET Image Process 2016; 10(6): 448-55.
[http://dx.doi.org/10.1049/iet-ipr.2015.0385]
[9]
Skin Cancer Statistics 2018: American Institute for Cancer Research; 2018. Available from: https://www.wcrf.org/dietandcancer/cancer-trends/skin-cancer-statistics
[10]
Fernandes SR, Estrela VV, Saotome O. On improving sub-pixel accuracy by means of B-spline. Proceedings of IEEE International Conference on Imaging Systems and Techniques (ist) 2014 Oct 14-17; Santorini, Greece New Jersey: IEEE 2014.
[http://dx.doi.org/10.1109/ist.2014.6958448]
[11]
Dey N, Rajinikanth V, Ashour AS, Tavares JMR. Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry (Basel) 2018; 10(2): 51.
[http://dx.doi.org/10.3390/sym10020051]
[12]
Razmjooy N, Mousavi BS, Soleymani F. A hybrid neural network imperialist competitive algorithm for skin color segmentation. Math Comput Model 2013; 57(3): 848-56.
[http://dx.doi.org/10.1016/j.mcm.2012.09.013]
[13]
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]
[14]
Rajpara SM, Botello AP, Townend J, Ormerod AD. Systematic review of dermoscopy and digital dermoscopy/artificial intelligence for the diagnosis of melanoma. Br J Dermatol 2009; 161(3): 591-604.
[http://dx.doi.org/10.1111/j.1365-2133.2009.09093.x] [PMID: 19302072]
[15]
Razmjooy N, Sheykhahmad FR, Ghadimi N. A hybrid neural network–world cup optimization algorithm for melanoma detection open med (wars) 2018; 13(1): 9-16.
[http://dx.doi.org/10.1515/med-2018-0002] [PMID: 29577090]
[16]
Tomatis S, Carrara M, Bono A, et al. Automated melanoma detection with a novel multispectral imaging system: results of a prospective study. Phys Med Biol 2005; 50(8): 1675-87.
[http://dx.doi.org/10.1088/0031-9155/50/8/004] [PMID: 15815089]
[17]
Farina B, Bartoli C, Bono A, et al. Multispectral imaging approach in the diagnosis of cutaneous melanoma: potentiality and limits. Phys Med Biol 2000; 45(5): 1243-54.
[http://dx.doi.org/10.1088/0031-9155/45/5/312] [PMID: 10843103]
[18]
Kuzmina I, Diebele I, Jakovels D, et al. Towards noncontact skin melanoma selection by multispectral imaging analysis. J Biomed Opt 2011; 16(6): 060502.
[http://dx.doi.org/10.1117/1.3584846] [PMID: 21721796]
[19]
Gutkowicz-Krusin D, Elbaum M, Greenebaum M, Jacobs A, Bogdan A. Systems and methods for the multispectral imaging and characterization of skin tissue. Google Patents. 2000.
[20]
Nehal KS, Oliveria SA, Marghoob AA, et al. Use of and beliefs about baseline photography in the management of patients with pigmented lesions: a survey of dermatology residency programmes in the United States. Melanoma Res 2002; 12(2): 161-7.
[http://dx.doi.org/10.1097/00008390-200204000-00009] [PMID: 11930113]
[21]
Slue W, Kopf AW, Rivers JK. Total-body photographs of dysplastic nevi. Arch Dermatol 1988; 124(8): 1239-43.
[http://dx.doi.org/10.1001/archderm.1988.01670080051017] [PMID: 3401028]
[22]
Feit NE, Dusza SW, Marghoob AA. Melanomas detected with the aid of total cutaneous photography. Br J Dermatol 2004; 150(4): 706-14.
[http://dx.doi.org/10.1111/j.0007-0963.2004.05892.x] [PMID: 15099367]
[23]
Welzel J. Optical coherence tomography in dermatology: A review. Skin Res Technol Rev 2001; 7(1): 1-9.
[http://dx.doi.org/10.1034/j.1600-0846.2001.007001001.x]
[24]
Mogensen M, Joergensen TM, Nürnberg BM, et al. Assessment of optical coherence tomography imaging in the diagnosis of non-melanoma skin cancer and benign lesions versus normal skin: observer-blinded evaluation by dermatologists and pathologists. Dermatol Surg 2009; 35(6): 965-72.
[http://dx.doi.org/10.1111/j.1524-4725.2009.01164.x] [PMID: 19397661]
[25]
Maher NG, Blumetti TP, Gomes EE, et al. Melanoma diagnosis may be a pitfall for optical coherence tomography assessment of equivocal amelanotic or hypomelanotic skin lesions. Br J Dermatol 2017; 177(2): 574-7.
[http://dx.doi.org/10.1111/bjd.15187] [PMID: 27861726]
[26]
Ferrante di Ruffano L, Dinnes J, Deeks JJ, et al. Optical coherence tomography for the diagnosis of skin cancer in adults. Cochrane Database Syst Rev. 2018.
[http://dx.doi.org/10.1002/14651858.CD013189]
[27]
Naylor MF, Zhou F, Geister BV, Nordquist RE, Li X, Chen WR. Treatment of advanced melanoma with laser immunotherapy and ipilimumab. J Biophotonics 2017; 10(5): 618-22.
[http://dx.doi.org/10.1002/jbio.201600271] [PMID: 28417565]
[28]
Ericson M. Laser Scanning microscopy targeting dermatology-insights from research and translational inertia microscopy histopathology and analytics. OSA Biophotonics Congress to Highlight Ever Increasing Role of Optics in Biology and Medicine 2018 Mar 3-6 Florida Washington The Optical Society 2018.
[29]
Heidari M, Sattarahmady N, Azarpira N, Heli H, Mehdizadeh AR, Zare T. Photothermal cancer therapy by gold-ferrite nanocomposite and near-infrared laser in animal model. Lasers Med Sci 2016; 31(2): 221-7.
[http://dx.doi.org/10.1007/s10103-015-1847-x] [PMID: 26694488]
[30]
Premkumar A, Marincola F, Taubenberger J, Chow C, Venzon D, Schwartzentruber D. Metastatic melanoma: correlation of MRI characteristics and histopathology. J Magn Reson Imaging 1996; 6(1): 190-4.
[http://dx.doi.org/10.1002/jmri.1880060134] [PMID: 8851427]
[31]
de Vries IJM, Lesterhuis WJ, Barentsz JO, et al. Magnetic resonance tracking of dendritic cells in melanoma patients for monitoring of cellular therapy. Nat Biotechnol 2005; 23(11): 1407-13.
[http://dx.doi.org/10.1038/nbt1154] [PMID: 16258544]
[32]
Lyng H, Haraldseth O, Rofstad EK. Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn. Reson. Med. 2000; 43(6): 828-36.
[http://dx.doi.org/10.1002/1522-2594(200006)43:6<828::AIDMRM8>3.0.CO;2-P] [PMID: 10861877]
[33]
Harland CC, Kale SG, Jackson P, Mortimer PS, Bamber JC. Differentiation of common benign pigmented skin lesions from melanoma by high-resolution ultrasound. Br J Dermatol 2000; 143(2): 281-9.
[http://dx.doi.org/10.1046/j.1365-2133.2000.03652.x] [PMID: 10951134]
[34]
Wang Y, Xu D, Yang S, Xing D. Toward in vivo biopsy of melanoma based on photoacoustic and ultrasound dual imaging with an integrated detector. Biomed Opt Express 2016; 7(2): 279-86.
[http://dx.doi.org/10.1364/BOE.7.000279] [PMID: 26977339]
[35]
Russo A, Mariotti C, Longo A, et al. Diffusion-weighted magnetic resonance imaging and ultrasound evaluation of choroidal melanomas after proton-beam therapy. Radiol Med (Torino) 2015; 120(7): 634-40.
[http://dx.doi.org/10.1007/s11547-015-0509-1] [PMID: 25650084]
[36]
Cohen VML, Pavlidou E, DaCosta J, et al. Staging uveal melanoma with whole-body positron-emission tomography/computed tomography and abdominal ultrasound: Low incidence of metastatic disease, high incidence of second primary cancers. Middle East Afr J Ophthalmol 2018; 25(2): 91-5.
[http://dx.doi.org/10.4103/meajo.MEAJO_96_18] [PMID: 30122854]
[37]
Zhang X-y, Zhang P-y. Ultrasound therapeutics-a review. Curr Med Imaging Rev 2017; 13(2): 162-5.
[http://dx.doi.org/10.2174/1573405612666160816142622]
[38]
Loupas T, McDicken W, Allan PL. An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans Circ Syst 1989; 36(1): 129-35.
[http://dx.doi.org/10.1109/31.16577]
[39]
Toprak A, Güler İ. Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter. Digit Signal Process 2007; 17(4): 711-23.
[http://dx.doi.org/10.1016/j.dsp.2006.11.008]
[40]
Rabbani H, Vafadust M, Abolmaesumi P, Gazor S. Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors. IEEE Trans Biomed Eng 2008; 55(9): 2152-60.
[http://dx.doi.org/10.1109/TBME.2008.923140] [PMID: 18713684]
[41]
Sanches JM, Nascimento JC, Marques JS. Medical image noise reduction using the Sylvester-Lyapunov equation. IEEE Trans Image Process 2008; 17(9): 1522-39.
[http://dx.doi.org/10.1109/TIP.2008.2001398] [PMID: 18701392]
[42]
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]
[43]
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]
[44]
Fiorese M, Peserico E, Silletti A. VirtualShave: automated hair removal from digital dermatoscopic images. Conf Proc IEEE Eng Med Biol Soc 2011; 2011: 5145-8.
[45]
Kang D, Kim S, Park S. Flow-guided hair removal for automated skin lesion identification. Multimedia Tools Appl 2018; 77(8): 9897-908.
[http://dx.doi.org/10.1007/s11042-018-5672-7]
[46]
Askar H, Schwendicke F, Lausch J, Meyer-Lueckel H, Paris S. Modified resin infiltration of non-, micro- and cavitated proximal caries lesions in vitro. J Dent 2018; 74: 56-60.
[http://dx.doi.org/10.1016/j.jdent.2018.03.010] [PMID: 29775637]
[47]
Scheffler M, Maturana E, Salomir R, Haller S, Kövari E. Air bubble artifact reduction in post-mortem whole-brain MRI: the influence of receiver bandwidth. Neuroradiology 2018; 60(10): 1089-92.
[http://dx.doi.org/10.1007/s00234-018-2071-8] [PMID: 30090981]
[48]
Sattar F, Floreby L, Salomonsson G, Lovstrom B. Image enhancement based on a nonlinear multiscale method. IEEE Trans Image Process 1997; 6(6): 888-95.
[http://dx.doi.org/10.1109/83.585239] [PMID: 18282982]
[49]
Zhu H, Chan FH, Lam FK. Image contrast enhancement by constrained local histogram equalization. Comput Vis Image Underst 1999; 73(2): 281-90.
[http://dx.doi.org/10.1006/cviu.1998.0723]
[50]
Abdullah-Al-Wadud M, Kabir MH, Dewan MAA, Chae O. A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 2007; 53(2): 593-600.
[http://dx.doi.org/10.1109/TCE.2007.381734]
[51]
Arici T, Dikbas S, Altunbasak Y. A histogram modification framework and its application for image contrast enhancement. IEEE Trans Image Process 2009; 18(9): 1921-35.
[http://dx.doi.org/10.1109/TIP.2009.2021548] [PMID: 19403363]
[52]
Zhou H, Chen M, Gass R, Rehg JM, Ferris L, Ho J. Feature-preserving artifact removal from dermoscopy images medical imaging 2008: image processing. Int Society Optic Photon 2008; 2008: 1-8.
[53]
Mahajan PR, Vyavahare MA. Artefact removal and contrast enhancement for dermoscopic images using image processing techniques. Int J Innov Res Elect Electron Instr Control Eng 2013; 1(9): 418-21.
[54]
Cheng H-D, Jiang XH, Sun Y, Wang J. Color image segmentation: advances and prospects. Pattern Recognit 2001; 34(12): 2259-81.
[http://dx.doi.org/10.1016/S0031-3203(00)00149-7]
[55]
Kechagias-Stamatis O, Aouf N, Nam D, Belloni C. Automatic x-ray image segmentation and clustering for threat detection target and background signatures III. Int Society Optic Photon 2017; 2017: 1-10.
[56]
Song S, Yu F, Zeng A, Chang AX, Savva M, Funkhouser T. Semantic scene completion from a single depth image. Conference on computer vision and pattern recognition; 2017 Jul 21-26; Hawaii.
[http://dx.doi.org/10.1109/CVPR.2017.28]
[57]
Fan H, Xie F, Li Y, Jiang Z, Liu J. Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold. Comput Biol Med 2017; 85: 75-85.
[http://dx.doi.org/10.1016/j.compbiomed.2017.03.025] [PMID: 28460258]
[58]
Bhuiyan MAH, Azad I, Uddin K. Image processing for skin cancer features extraction. Int J Sci Eng Res 2013; 4(2): 1-6.
[59]
Premaladha J, Priya ML, Sujitha S, Ravichandran K. Normalised Otsu’s segmentation algorithm for melanoma diagnosis. Indian J Sci and Techn 2015; 8(22): 1-6.
[http://dx.doi.org/10.17485/ijst/2015/v8i22/79140]
[60]
Razmjooy N, Mousavi BS, Soleymani F, Khotbesara MH. A computer-aided diagnosis system for malignant melanomas. Neural Comput Appl 2013; 23(7-8): 2059-71.
[http://dx.doi.org/10.1007/s00521-012-1149-1]
[61]
Ebrahimi SMS, Pourghassem H, Keshavarz A. Segmentation of melanoma and other pigmented skin lesions in dermoscopic images using fusion of threshoding methods based on reinforcement algorithm. J Intell Proc Electr Technol 2014; 4(16): 37-48.
[62]
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]
[63]
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]
[64]
Barata C, Ruela M, Francisco M, Mendonça T, Marques JS. Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst J 2014; 8(3): 965-79.
[http://dx.doi.org/10.1109/JSYST.2013.2271540]
[65]
Yüksel ME, Borlu M. Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic. IEEE Trans Fuzzy Syst 2009; 17(4): 976-82.
[http://dx.doi.org/10.1109/TFUZZ.2009.2018300]
[66]
Lee H, Chen Y-PP. Skin cancer extraction with optimum fuzzy thresholding technique. Appl Intell 2014; 40(3): 415-26.
[http://dx.doi.org/10.1007/s10489-013-0474-0]
[67]
Sookpotharom S. Border detection of skin lesion images based on fuzzy C-means thresholding. Third international conference on genetic and evolutionary computing; 2009 Oct 14-17; Guilin, China. Berlin: IEEE 2010.
[http://dx.doi.org/10.1109/WGEC.2009.96]
[68]
Xie F-Y, Qin S-Y, Jiang Z-G, Meng R-S. 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]
[69]
Yuan X, Situ N, Zouridakis G. A narrow band graph partitioning method for skin lesion segmentation. Pattern Recognit 2009; 42(6): 1017-28.
[http://dx.doi.org/10.1016/j.patcog.2008.09.006]
[70]
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]
[71]
Rajab MI, Woolfson MS, Morgan SP. Application of region-based segmentation and neural network edge detection to skin lesions. Comput Med Imaging Graph 2004; 28(1-2): 61-8.
[http://dx.doi.org/10.1016/S0895-6111(03)00054-5] [PMID: 15127750]
[72]
Varatharajan R. Soft computing in image processing and its applications. Curr Med Imaging Rev 2017; 13(3): 222.
[http://dx.doi.org/10.2174/157340561303170720193000]
[73]
Patwardhan SV, Dai S, Dhawan AP. Multi-spectral image analysis and classification of melanoma using fuzzy membership based partitions. Comput Med Imaging Graph 2005; 29(4): 287-96.
[http://dx.doi.org/10.1016/j.compmedimag.2004.11.001] [PMID: 15890256]
[74]
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]
[75]
Ramteke NS, Jain SV. Analysis of skin cancer using fuzzy and wavelet technique-review & proposed new algorithm. Int J Engineer Trend Technol 2013; 4(6): 2555-66.
[76]
Ercal F, Chawla A, Stoecker WV, Lee H-C, Moss RH. Neural network diagnosis of malignant melanoma from color images. IEEE Trans Biomed Eng 1994; 41(9): 837-45.
[http://dx.doi.org/10.1109/10.312091] [PMID: 7959811]
[77]
Amartur SC, Piraino D, Takefuji Y. Optimization neural networks for the segmentation of magnetic resonance images. IEEE Trans Med Imaging 1992; 11(2): 215-20.
[http://dx.doi.org/10.1109/42.141645] [PMID: 18218375]
[78]
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115-8.
[http://dx.doi.org/10.1038/nature21056 PMID: 28117445]
[79]
Yu L, Chen H, Dou Q, Qin J, Heng P-A. 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]
[80]
Mukherjee S, Adhikari A, Roy M. Malignant melanoma detection using multi layer perceptron with optimized network parameter selection by PSO contemporary. Advances in innovative and applicable information technology. Berlin Springer. 2019; pp. 101-9.
[81]
Bakheet S. An SVM framework for malignant melanoma detection based on optimized hog features. Computation 2017; 5(1): 1-4.
[http://dx.doi.org/10.3390/computation5010004]
[82]
Li Y. Lesion segmentation in dermoscopy images using particle swarm optimization and Markov random field. 30th International symposium on Computer-Based Medical Systems (CBMS) 2017 June 22-24 Thessaloniki, Greece. New Jeresy: IEEE 2017.
[83]
Song G, Han J, Zhao Y, Wang Z, Du H. A review on medical image registration as an optimization problem. Curr Med Imaging Rev 2017; 13(3): 274-83.
[http://dx.doi.org/10.2174/1573405612666160920123955] [PMID: 28845149]
[84]
Ferri M, Tomba I, Visotti A, Stanganelli I. A feasibility study for a persistent homology-based k-nearest neighbor search algorithm in melanoma detection. J Math Imaging Vis 2017; 57(3): 324-39.
[http://dx.doi.org/10.1007/s10851-016-0680-6]
[85]
Dalila F, Zohra A, Reda K, Hocine C. Segmentation and classification of melanoma and benign skin lesions. Optik (Stuttg) 2017; 140: 749-61.
[http://dx.doi.org/10.1016/j.ijleo.2017.04.084]
[86]
Jadhav AR, Ghontale AG, Shrivastava VK. Segmentation and border detection of melanoma lesions using convolutional neural network and SVM. Computational intelligence: theories, applications and future directions-volume I. Berlin Springer. 2019; pp. 97-108.
[87]
Gautam D, Ahmed M. Melanoma detection and classification using SVM based decision support system. Annual IEEE India Conference (INDICON) 2015 Dec 17-20; New Delhi, India. New Jersey: IEEE. 2016.
[http://dx.doi.org/10.1109/INDICON.2015.7443447]
[88]
Mukherjee S, Adhikari A, Roy M. Melanoma identification using MLP with parameter selected by metaheuristic algorithms Intelligent innovations in multimedia data engineering and management. Pennsylvania: IGI Global 2018.
[89]
Agarwal A, Issac A, Dutta MK, Riha K, Uher V. Automated skin lesion segmentation using K-Means clustering from digital dermoscopic images. 40th International Conference on Telecommunications and Signal Processing (TSP) 2017 July 5-7 Barcelona, Spain. New Jersey: IEEE 2017.
[http://dx.doi.org/10.1109/TSP.2017.8076087]
[90]
Xu H, Mandal M. Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm. EURASIP J Image Video Process 2015; 2015(1): 18.
[http://dx.doi.org/10.1186/s13640-015-0076-3]
[91]
Alvarez D, Iglesias M. K-means clustering and ensemble of regressions: an algorithm for the ISIC 2017 skin lesion segmentation challenge. arXiv preprint arXiv:170207333 2017.
[92]
Xie F, Bovik AC. Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm. Pattern Recognit 2013; 46(3): 1012-9.
[http://dx.doi.org/10.1016/j.patcog.2012.08.012]
[93]
Mehta A, Parihar AS, Mehta N. editors. Supervised classification of dermoscopic images using optimized fuzzy clustering based multi-layer feed-forward neural network. International Conference on Computer, Communication and Control (IC4); 2015 Sept 10-12; Indore, India. New Jersey: IEEE 2016.
[94]
Moallem P, Razmjooy N. Optimal threshold computing in automatic image thresholding using adaptive particle swarm optimization. J Appl Res Technol 2012; 10(5): 703-12.
[http://dx.doi.org/10.22201/icat.16656423.2012.10.5.361]
[95]
Razmjooy N, Mousavi BS, Sargolzaei P, Soleymani F. Image thresholding based on evolutionary algorithms. Int J Phys Sci 2011; 6(31): 7203-11.
[96]
Razmjooy N, Mousavi BS, Khalilpour M, Hosseini H. Automatic selection and fusion of color spaces for image thresholding. Signal Image Video Process 2014; 8(4): 603-14.
[http://dx.doi.org/10.1007/s11760-012-0303-7]
[97]
Deng D, Nikolov P, Arevalo HJ, Trayanova NA. Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models. Comput Biol Med 2018; 102: 426-32.
[http://dx.doi.org/10.1016/j.compbiomed.2018.09.031] [PMID: 30301573]
[98]
Pratondo A, Chui C-K, Ong S-H. Integrating machine learning with region-based active contour models in medical image segmentation. J Vis Commun Image Represent 2017; 43: 1-9.
[http://dx.doi.org/10.1016/j.jvcir.2016.11.019]
[99]
Li F, Clausi DA, Xu L, Wong A. ST-IRGS: A region-based self-training algorithm applied to hyperspectral image classification and segmentation. IEEE Trans Geosci Remote Sens 2018; 56(1): 3-16.
[http://dx.doi.org/10.1109/TGRS.2017.2713123]
[100]
Moallem P, Razmjooy N. A multi layer perceptron neural network trained by invasive weed optimization for potato color image segmentation. Trends Appl Sci Res 2012; 7(6): 445.
[http://dx.doi.org/10.3923/tasr.2012.445.455]
[101]
Moallem P, Razmjooy N, Ashourian M. Computer vision-based potato defect detection using neural networks and support vector machine. Int J Robot Autom 2013; 28(2): 137-45.
[http://dx.doi.org/10.2316/Journal.206.2013.2.206-3746]
[102]
Mousavi BS, Soleymani F, Razmjooy N. Color image segmentation using neuro-fuzzy system in a novel optimized color space. Neural Comput Appl 2013; 23(5): 1513-20.
[http://dx.doi.org/10.1007/s00521-012-1102-3]
[103]
Moallem P, Razmjooy N, Mousavi B. Robust potato color image segmentation using adaptive fuzzy inference system. Iranian J Fuzzy Systems 2014; 11(6): 47-65.
[104]
Zhao F, Liu H, Fan J, Chen CW, Lan R, Li N. Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for image segmentation. Neurocomputing 2018; 312(27): 296-309.
[http://dx.doi.org/10.1016/j.neucom.2018.05.116]
[105]
Mastorakis N. Neural network methods for image segmentation. Proceedings of 2nd International Conference on Applied Physics, System Science and Computers (APSAC2017). 2017 Sep 27-29 Dubrovnik, Croatia. Berlin: Springer. 2017.
[106]
Dey N, Ashour AS. Meta-heuristic algorithms in medical image segmentation: A review. Advancements in Applied Metaheuristic Computing. IGI Global 2018; pp. 185-203.
[http://dx.doi.org/10.4018/978-1-5225-4151-6.ch008]
[107]
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation International conference on medical image computing and computer-assisted intervention; 2015 Oct 5-9; Munich, Germany. Berlin: Springer 2015.
[108]
Zhu F, Bosch M, Khanna N, Boushey CJ, Delp EJ. Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE J Biomed Health Inform 2015; 19(1): 377-88.
[http://dx.doi.org/10.1109/JBHI.2014.2304925] [PMID: 25561457]
[109]
Jain S, Pise N. Computer aided melanoma skin cancer detection using image processing. Procedia Comput Sci 2015; 48: 735-40.
[http://dx.doi.org/10.1016/j.procs.2015.04.209]
[110]
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]
[111]
Thakur N, Juneja M. Clustering based approach for segmentation of optic cup and optic disc for detection of glaucoma. Curr Med Imaging Rev 2017; 13(1): 99-105.
[http://dx.doi.org/10.2174/1573405612666160606124044]
[112]
Celebi ME, Aslandogan YA, Bergstresser PR. Unsupervised border detection of skin lesion images. International conference on Information Technology: Coding and Computing (ITCC'05) - Volume II 2005 Apr 4-6 Las Vegas, NV, USA New Jersey IEEE 2005.
[113]
Dhawan AP, Sicsu A. Segmentation of images of skin lesions using color and texture information of surface pigmentation. Comput Med Imaging Graph 1992; 16(3): 163-77.
[http://dx.doi.org/10.1016/0895-6111(92)90071-G] [PMID: 1623492]
[114]
Barata C, Ruela M, Mendonça T, Marques JS. A bag-of-features approach for the classification of melanomas in dermoscopy images: The role of color and texture descriptors Computer vision techniques for the diagnosis of skin cancer. Berlin: Springer 2014; pp. 49-69.
[115]
Riaz F, Hassan A, Nisar R, Dinis-Ribeiro M, Coimbra MT. Content-adaptive region-based color texture descriptors for medical images. IEEE J Biomed Health Inform 2017; 21(1): 162-71.
[http://dx.doi.org/10.1109/JBHI.2015.2492464] [PMID: 26513811]
[116]
Tajeddin NZ, Asl BM. Melanoma recognition in dermoscopy images using lesion’s peripheral region information. Comput Methods Programs Biomed 2018; 163: 143-53.
[http://dx.doi.org/10.1016/j.cmpb.2018.05.005] [PMID: 30119849]
[117]
Bharathi VS, Ganesan L. Orthogonal moments based texture analysis of CT liver images. Pattern Recognit Lett 2008; 29(13): 1868-72.
[http://dx.doi.org/10.1016/j.patrec.2008.06.003]
[118]
Ali A-R, Vacavant A, Grand-Brochier M, Albouy-Kissi A, Boire J-Y. A fuzzy approach to liver tumor segmentation with Zernike Moments. Int J Med Health Biomed Bioengineer Pharmaceut Engineer 2015; 9(7): 559-64.
[119]
Nogueira MA, Abreu PH, Martins P, Machado P, Duarte H, Santos J. Image descriptors in radiology images: a systematic review. Artif Intell Rev 2017; 47(4): 531-59.
[http://dx.doi.org/10.1007/s10462-016-9492-8]
[120]
Sharma S, Khanna P. Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. J Digit Imaging 2015; 28(1): 77-90.
[http://dx.doi.org/10.1007/s10278-014-9719-7] [PMID: 25005867]
[121]
Kastl L, Kemper B, Lloyd GR, et al. Performance of mid infrared spectroscopy in skin cancer cell type identification Optical biopsy XV: toward real-time spectroscopic imaging and diagnosis. Proc SPIE 2017; 10060: 1-10.
[122]
Ruela M, Barata C, Marques JS, Rozeira J. A system for the detection of melanomas in dermoscopy images using shape and symmetry features. Comp Methods Biomech Biomed Eng Vis 2017; 5(2): 127-37.
[123]
Guerra-Segura E, Travieso-González CM, Alonso-Hernández JB, Ravelo-García AG, Carretero G. Symmetry extraction in high sensitivity melanoma diagnosis. Symmetry (Basel) 2015; 7(2): 1061-79.
[http://dx.doi.org/10.3390/sym7021061]
[124]
Kumar S, Sharma D, Yadav G, Singh HP, Gupta A. Possibilities of melanoma by extracting all asymmetric features. International conference on Intelligent Circuits and Systems (ICICS) 2018 Apr 19-20 Phagwara, India. New Jersey: IEEE 2018.
[http://dx.doi.org/10.1109/ICICS.2018.00057]
[125]
Tanaka T, Torii S, Kabuta I, Shimizu K, Tanaka M. Pattern classification of nevus with texture analysis. IEEJ Trans Electr Electron Eng 2008; 3(1): 143-50.
[http://dx.doi.org/10.1002/tee.20246]
[126]
Mendoza CS, Serrano C, Acha B. Scale invariant descriptors in pattern analysis of melanocytic lesions. 16th IEEE International Conference on Image Processing (ICIP) 2009 Nov 7-10 Cairo, Egypt. New Jersey: IEEE 2010.
[http://dx.doi.org/10.1109/ICIP.2009.5414525]
[127]
Abbas Q, Celebi ME, Fondón I. Computer-aided pattern classification system for dermoscopy images. Skin Res Technol 2012; 18(3): 278-89.
[http://dx.doi.org/10.1111/j.1600-0846.2011.00562.x] [PMID: 22093020]
[128]
Khan NY, McCane B, Wyvill G, Eds. SIFT and SURF performance evaluation against various image deformations on benchmark dataset. International conference on Digital Image Computing: Techniques and Applications (DICTA) 2011 Dec 6-8 Noosa, QLD, Australia. New Jersey: IEEE 2011.
[http://dx.doi.org/10.1109/DICTA.2011.90]
[129]
Kavitha J, Suruliandi A, Nagarajan D, Nadu T. Melanoma detection in dermoscopic images using global and local feature extraction. Int J Multimedia Ubiquit Eng 2017; 12(5): 19-28.
[http://dx.doi.org/10.14257/ijmue.2017.12.5.02]
[130]
Celebi ME, Iyatomi H, Schaefer G, Stoecker WV. Lesion border detection in dermoscopy images. Comput Med Imaging Graph 2009; 33(2): 148-53.
[http://dx.doi.org/10.1016/j.compmedimag.2008.11.002] [PMID: 19121917]
[131]
Narasimhan K, Elamaran V. Wavelet-based energy features for diagnosis of melanoma from dermoscopic images. Int J Biomed Eng Technol 2016; 20(3): 243-52.
[http://dx.doi.org/10.1504/IJBET.2016.075427]
[132]
Sadri AR, Azarianpour S, Zekri M, Celebi ME, Sadri S. WN-based approach to melanoma diagnosis from dermoscopy images. IET Image Process 2017; 11(7): 475-82.
[http://dx.doi.org/10.1049/iet-ipr.2016.0681]
[133]
Chatterjee S, Dey D, Munshi S. Optimal selection of features using wavelet fractal descriptors and automatic correlation bias reduction for classifying skin lesions. Biomed Signal Process Control 2018; 40: 252-62.
[http://dx.doi.org/10.1016/j.bspc.2017.09.028]
[134]
Masood A, Ali Al-Jumaily A. Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms Int J Biomedical Imaging 2013 2013.
[http://dx.doi.org/10.1155/2013/323268]
[135]
Anbeek P, Vincken KL, Viergever MA. Automated MS-lesion segmentation by k-nearest neighbor classification. MIDAS Journal 2008. Available from: https://www.midasjournal.org/browse/publication/610
[136]
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. Color Med Image Anal 2013; 2013: 1-26.
[137]
Steenwijk MD, Pouwels PJ, Daams M, et al. Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). Neuroimage Clin 2013; 3: 462-9.
[http://dx.doi.org/10.1016/j.nicl.2013.10.003] [PMID: 24273728]
[138]
Meinel LA, Stolpen AH, Berbaum KS, Fajardo LL, Reinhardt JM. Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system. J Magn Reson Imaging 2007; 25(1): 89-95.
[http://dx.doi.org/10.1002/jmri.20794] [PMID: 17154399]
[139]
Xie F, Fan H, Li Y, Jiang Z, Meng R, Bovik A. Melanoma classification on dermoscopy images using a neural network ensemble model. IEEE Trans Med Imaging 2017; 36(3): 849-58.
[http://dx.doi.org/10.1109/TMI.2016.2633551] [PMID: 27913337]
[140]
Roffman D, Hart G, Girardi M, Ko CJ, Deng J. Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network. Sci Rep 2018; 8(1): 1701.
[http://dx.doi.org/10.1038/s41598-018-19907-9] [PMID: 29374196]
[141]
Kulkarni A, Mukhopadhyay D. SVM Classifier based melanoma image classification. Res J Pharm Technol 2017; 10(12): 4391-2.
[http://dx.doi.org/10.5958/0974-360X.2017.00808.3]
[142]
Sana MS, Khan S. Dermoscopy images classification based on color, texture and shape features using SVM. The 3rd International Conference on Next Generation Computing(ICNGC2017b); 2017 Dec 21-24; Kaohsiung, Taiwan.
[143]
Moreno-Ramírez D, Tejera-Vaquerizo A, Mendonça FI, Ojeda-Vila T, Ferrándiz L. Making decisions on sentinel lymph node biopsy for malignant melanoma: prioritization of determinants using a decision tree. J Eur Acad Dermatol Venereol 2017; 31(5): e247-9.
[http://dx.doi.org/10.1111/jdv.14019] [PMID: 27785829]
[144]
Ohki K, Celebi ME, Schaefer G, Iyatomi H, Eds. Building of readable decision trees for automated melanoma discrimination 11th International Symposium on Visual Computing 2015 Dec 14-16 Las Vegas, NV, USA Berlin: Springer. 2015.
[http://dx.doi.org/10.1007/978-3-319-27863-6_67]
[145]
Lorenzo D, Ochoa M, Piulats JM, et al. Prognostic factors and decision tree for long-term survival in metastatic uveal melanoma. Cancer Res. Treat. 2018; 50(4): 1130-9.
[http://dx.doi.org/10.4143/crt.2017.171] [PMID: 29198096]
[146]
Lingala M, Stanley RJ, Rader RK, et al. Fuzzy logic color detection: Blue areas in melanoma dermoscopy images. Comput Med Imaging Graph 2014; 38(5): 403-10.
[http://dx.doi.org/10.1016/j.compmedimag.2014.03.007] [PMID: 24786720]
[147]
Kaur A, Kaur H. A noval approach to detect and classify skin disease by analysing MRI images using fuzzy support vector machine (FSVM). Int J Eng Technol Comput Res 2017; 5(5). Available from: https://www.ijetcr.org/index.php/ijetcr/article/view/448
[148]
Almubarak HA, Stanley RJ, Stoecker WV, Moss RH. Fuzzy color clustering for melanoma diagnosis in dermoscopy Images. Inf 2017; 8(3): 89.
[http://dx.doi.org/10.3390/info8030089]
[149]
Merler S, Furlanello C, Larcher B, Sboner A. Automatic model selection in cost-sensitive boosting. Inf Fusion 2003; 4(1): 3-10.
[http://dx.doi.org/10.1016/S1566-2535(02)00100-8]
[150]
Capdehourat G, Corez A, Bazzano A, Musé P. Pigmented skin lesions classification using dermatoscopic images. Progress in pattern recognition, image analysis, computer vision, and applications; 2009 Nov 15-18; Guadalajara, Jalisco, Mexico. Berlin: Springer 2009.
[151]
Codella NC, Nguyen Q-B, Pankanti S, Gutman D, et al. Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J Res Dev 2017; 61: 1-15.
[http://dx.doi.org/10.1147/JRD.2017.2708299]
[152]
Hemanth DJ, Estrela VV. Deep learning for image processing applications. IOS Press: Amsterdam 2017.
[153]
Razmjooy N, Estrela VV, Loschi HJ. A study on metaheuristic-based neural networks for image segmentation purposes. Data science theory, analysis and applications. Taylor and Francis: Abingdon. 2019.
[http://dx.doi.org/10.1201/9780429263798-2]
[154]
Cruz BF, de Assis JT, Estrela VV, Khelassi A. A Compact SIFT-based strategy for visual information retrieval in large image databases. Med Techn J 2019; 3,2: 402-12.
[155]
Estrela VV, Franz MO, et al. Adaptive mixed norm optical flow estimation. Proceedings of Visual Communications and Image Processing; 2005 June 24; Beijing, China. Bellingham: SPIE 2005.
[http://dx.doi.org/10.1117/12.632674]
[156]
Estrela VV, Rivera LA, Beggio PC, Lopes RT. Regularized pel-recursive motion estimation using generalized cross-validation and spatial adaptation. 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003) 2003 Oct 12-15 Sao Carlos, Brazil New Jersey: IEEE 2003.
[http://dx.doi.org/10.1109/SIBGRA.2003.1241027]
[157]
Estrela VV, Coelho AM. State-of-the art motion estimation in the context of 3D TV multimedia networking and coding. Hershey, PA: IGI Global 2013; pp. 148-73.
[http://dx.doi.org/10.4018/978-1-4666-2660-7.ch006]
[158]
Coelho AM, Estrela VV. Data-driven motion estimation with spatial adaptation. Intl J of Image Proc (IJIP) 2012; 6(1): 53-67.
[159]
Rivera LA, Estrela VV, Carvalho PCP. Oriented bounding boxes using multiresolution contours for fast interference detection of arbitrary geometry objects. 12th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2004; 2004 Feb 2-6; University of West Bohemia, Campus Bory, Plzen-Bory, Czech Republic. arXiv:1611.03666.
[160]
Marins HR, Estrela VV. On the use of motion vectors for 2D and 3D error concealment in H264/AVC video feature detectors and motion detection in video processing. Hershey, PA: IGI Global 2017; pp. 164-86.
[http://dx.doi.org/10.4018/978-1-5225-1025-3.ch008]
[161]
de Jesus MA, Estrela VV. Optical flow estimation using total least squares variants. Orient J Comput Sci Technol 2017; 10: 563-79.
[http://dx.doi.org/10.13005/ojcst/10.03.03]
[162]
Coelho AM, Assis JT, Estrela VV. Error concealment by means of clustered blockwise PCA. IEEE PCS 2009; 2009: 1-4.
[http://dx.doi.org/10.1109/PCS.2009.5167442]

Rights & Permissions Print Cite
Article Metrics
18
© 2024 Bentham Science Publishers | Privacy Policy