ABCD Feature Extraction for Melanoma Screening Using Image Processing: A review
Keywords:
Melanoma, Image Processing, Feature Extraction, ABCD rulesAbstract
Melanoma is an uncommon type of skin cancer. Once cancer cells spread to internal organs, the disease becomes deadly with high mortality rate. Early diagnosis can reduce disease progression as well as death rate from melanoma. For precise diagnosis of the disease at the early stage, specialized physicians are needed. Unfortunately, only a handful of these experts are available. Therefore, applying cutting-edge technologies to melanoma screening and diagnosis can help minimizing death of patients and reduces workloads of physicians. Image processing is a popular branch of data science for differentiating images using their features, i.e., size, color, margin. Image processing composes of 5 steps, including data acquisition, data preprocessing, feature extraction, modeling, and evaluation. During feature extraction, ABCD rule, which stands for asymmetry, border, color, and diameter, respectively, is widely applied due to its popularity and accuracy. In the current review, we aimed to elaborate image processing protocol for screening of melanoma with ABCD feature extraction. The detailed methods for performing feature extraction using ABCD rules are summarized. This review would be beneficial for scientists who interest in this field, and for future development of innovation to improve health of the patients and the public.
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Jain S, jagtap V, Pise N. Computer Aided Melanoma Skin Cancer Detection Using Image Processing. Procedia Comput Sci. 2015;48:735-740. https://doi.org/10.1016/j.procs.2015.04.209
Chang JWC, Guo J, Hung CY, et al. Sunrise in melanoma management: Time to focus on melanoma burden in Asia. Asia Pac J Clin Oncol. 2017;13(6):423-427. https://doi.org/10.1111/ajco.12670
Holmes GA, Vassantachart JM, Limone BA, Zumwalt M, Hirokane J, Jacob SE. Using Dermoscopy to Identify Melanoma and Improve Diagnostic Discrimination. Fed Pract. 2018;35(Suppl 4):S39-S45.
Argenziano G, Soyer HP, Chimenti S, et al. Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J Am Acad Dermatol. 2003;48(5):679-693. https://doi.org/10.1067/mjd.2003.281
Vijayalakshmi M M. Melanoma Skin Cancer Detection using Image Processing and Machine Learning. Int J Trend Sci Res Dev. 2019;3(4):780-784. https://doi.org/10.31142/ijtsrd23936
Zhang N, Cai YX, Wang YY, Tian YT, Wang XL, Badami B. Skin cancer diagnosis based on optimized convolutional neural network. Artif Intell Med. 2020;102:101756. https://doi.org/10.1016/j.artmed.2019.101756
Haenssle HA, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol Off J Eur Soc Med Oncol. 2018;29(8):1836-1842. https://doi.org/10.1093/annonc/mdy166
Li Y, Shen L. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network. Sensors. 2018;18(2):E556. https://doi.org/10.3390/s18020556
Seeja R D, Suresh A. Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM). Asian Pac J Cancer Prev APJCP. 2019;20(5):1555-1561. https://doi.org/10.31557/APJCP.2019.20.5.1555
Rehman A, Khan MA, Mehmood Z, Saba T, Sardaraz M, Rashid M. Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction. Microsc Res Tech. 2020;83(4):410-423. https://doi.org/10.1002/jemt.23429
Daniel Jensen J, Elewski BE. The ABCDEF Rule: Combining the “ABCDE Rule” and the “Ugly Duckling Sign” in an Effort to Improve Patient Self-Screening Examinations. J Clin Aesthetic Dermatol. 2015;8(2):15.
Thanh DNH, Prasath VBS, Hieu LM, Hien NN. Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Colour Normalisation and Feature Extraction with the ABCD Rule. J Digit Imaging. 2020;33(3):574-585. https://doi.org/10.1007/s10278-019-00316-x
Amoabedini A, Farsani MS, Saberkari H, Aminian E. Employing the Local Radon Transform for Melanoma Segmentation in Dermoscopic Images. J Med Signals Sens. 2018;8(3):184-194. https://doi.org/10.4103/jmss.JMSS_40_17
Isasi AG, Zapirain BG, Zorrilla AM. Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms. Comput Biol Med. 2011;41(9):742-755. https://doi.org/10.1016/j.compbiomed.2011.06.010
Dildar M, Akram S, Irfan M, et al. Skin Cancer Detection: A Review Using Deep Learning Techniques. Int J Environ Res Public Health. 2021;18(10):5479. https://doi.org/10.3390/ijerph18105479
Mendonca T, Ferreira PM, Marçal ARS, et al. PH2: A Public Database for the Analysis of Dermoscopic Images. In: Celebi ME, Mendonca T, Marques JS, eds. Dermoscopy Image Analysis. Melanoma Skin Cancer Detection. CRC Press; 2017.
International Skin Imaging Collaboration. SIIM-ISIC 2020 Challenge Dataset. Published online 2020. https://doi.org/10.34970/2020-DS01
Vocaturo E, Zumpano E, Veltri P. Image pre-processing in computer vision systems for melanoma detection. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). ; 2018:2117-2124. https://doi.org/10.1109/BIBM.2018.8621507
Hoshyar AN, Al-Jumaily A, Hoshyar AN. The Beneficial Techniques in Preprocessing Step of Skin Cancer Detection System Comparing. Procedia Comput Sci. 2014;42:25-31. https://doi.org/10.1016/j.procs.2014.11.029
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):533-543. https://doi.org/10.1016/s0010-4825(97)00020-6
Wati M, Haviluddin, Puspitasari N, Budiman E, Rahim R. First-order Feature Extraction Methods for Image Texture and Melanoma Skin Cancer Detection. J Phys: Conf Ser. 2019;1230(1):012013. https://doi.org/10.1088/1742-6596/1230/1/012013
Bangare SL, Dubal A, Bangare PS, Patil ST. Reviewing Otsu’s Method For Image Thresholding. Int J Appl Eng Res. 2015;10(9):21777-21783. https://doi.org/10.37622/IJAER/10.9.2015.21777-21783
Majumder S, Ullah MA. Feature extraction from dermoscopy images for melanoma diagnosis. SN Appl Sci. 2019;1(7):753. https://doi.org/10.1007/s42452-019-0786-8
Zghal NS, Derbel N. Melanoma Skin Cancer Detection based on Image Processing. Curr Med Imaging Rev. 2020;16(1):50-58. https://doi.org/10.2174/1573405614666180911120546
Uddin MK, Azad I, Bhuiyan A. Image Processing for Skin Cancer Features Extraction. Int J Sci Eng Res. 2013;4(2):1-6.
Rigel DS, Russak J, Friedman R. The evolution of melanoma diagnosis: 25 years beyond the ABCDs. CA Cancer J Clin. 2010;60(5):301-316. https://doi.org/10.3322/caac.20074
Fu’adah YN, Pratiwi NC, Pramudito MA, Ibrahim N. Convolutional Neural Network (CNN) for Automatic Skin Cancer Classification System. IOP Conf Ser: Mater Sci Eng. 2020;982:012005. https://doi.org/10.1088/1757-899X/982/1/012005
Naranjo-Torres J, Mora M, Hernández-García R, Barrientos RJ, Fredes C, Valenzuela A. A Review of Convolutional Neural Network Applied to Fruit Image Processing. Appl Sci. 2020;10(10):3443. https://doi.org/10.3390/app10103443
Sagar A, Jacob D. Convolutional Neural Networks for Classifying Melanoma Images. Cancer Biology; 2020. https://doi.org/10.1101/2020.05.22.110973
Nasiri S, Helsper J, Jung M, Fathi M. DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images. BMC Bioinformatics. 2020;21(Suppl 2):84. https://doi.org/10.1186/s12859-020-3351-y
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