{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T07:56:23Z","timestamp":1769759783368,"version":"3.49.0"},"reference-count":66,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T00:00:00Z","timestamp":1519257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu\/Kapur based thresholding technique and the image post-processing step using the level set\/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard\u2019s coefficient, Dice\u2019s coefficient, false positive\/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur\u2019s thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy.<\/jats:p>","DOI":"10.3390\/sym10020051","type":"journal-article","created":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T12:06:18Z","timestamp":1519301178000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":131,"title":["Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images"],"prefix":"10.3390","volume":"10","author":[{"given":"Nilanjan","family":"Dey","sequence":"first","affiliation":[{"name":"Department of Information Technology, Techno India College of Technology, Kolkata 700156, West Bengal, India"}]},{"given":"Venkatesan","family":"Rajinikanth","sequence":"additional","affiliation":[{"name":"Department of Electronics and Instrumentation Engineering, St. Joseph\u2019s College of Engineering, Chennai 600119, Tamilnadu, India"}]},{"given":"Amira","family":"Ashour","sequence":"additional","affiliation":[{"name":"Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta 31527, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7603-6526","authenticated-orcid":false,"given":"Jo\u00e3o Manuel","family":"Tavares","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Departamento de Engenharia Mec\u00e2nica, Faculdade de Engenharia, Universidade do Porto, Porto, Rua Dr. Roberto Frias s\/n, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,22]]},"reference":[{"key":"ref_1","first-page":"65","article-title":"A survey on melanoma diagnosis using image processing and soft computing techniques","volume":"6","author":"Premaladha","year":"2014","journal-title":"Res. J. Inf. Tech."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/JSYST.2014.2313671","article-title":"Automated quantification of clinically significant colors in dermoscopy images and its application to skin lesion classification","volume":"8","author":"Celebi","year":"2014","journal-title":"IEEE Syst. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compmedimag.2008.11.002","article-title":"Lesion border detection in dermoscopy images","volume":"33","author":"Celebi","year":"2009","journal-title":"Comput. Med. Imag. Graph."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.compmedimag.2007.01.003","article-title":"A methodological approach to the classification of dermoscopy images","volume":"31","author":"Celebi","year":"2007","journal-title":"Comput. Med. Imag. Graph."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Amelard, R., Glaister, J., Wong, A., and Clausi, D.A. (2013). Melanoma decision support using lighting-corrected intuitive feature models. Comput. Vis. Tech. Diagn. Skin Cancer, 193\u2013219.","DOI":"10.1007\/978-3-642-39608-3_7"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/S0190-9622(94)70061-3","article-title":"The ABCD rule of dermatoscopy: High prospective value in the diagnosis of doubtful melanocytic skin lesions","volume":"30","author":"Nachbar","year":"1994","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_7","first-page":"1146","article-title":"Improving dermoscopy image classification using color constancy","volume":"19","author":"Barata","year":"2015","journal-title":"IEEE J. Biomed. Health. Inform."},{"key":"ref_8","first-page":"1837","article-title":"Otsu\u2019s multi-thresholding and active contour snake model to segment dermoscopy images","volume":"7","author":"Rajinikanth","year":"2017","journal-title":"J. Med. Imag. Health Inf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/TBME.2014.2365518","article-title":"High-level intuitive features (HLIFs) for intuitive skin lesion description","volume":"62","author":"Amelard","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1109\/TBME.2013.2297622","article-title":"Segmentation of skin lesions from digital images using joint statistical texture distinctiveness","volume":"61","author":"Glaister","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1109\/TBME.2013.2244596","article-title":"MSIM: Multistage illumination modeling of dermatological photographs for illumination-corrected skin lesion analysis","volume":"60","author":"Glaister","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cmpb.2016.03.032","article-title":"Computational methods for the image segmentation of pigmented skin lesions: A review","volume":"131","author":"Oliveira","year":"2016","journal-title":"Comput. Method. Progr. Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mercedes Filho, M., Ma, Z., and Tavares, J.M.R.S. (2015). A Review of the quantification and classification of pigmented skin lesions: From dedicated to hand-held devices. J. Med. Syst., 39.","DOI":"10.1007\/s10916-015-0354-8"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/JBHI.2015.2390032","article-title":"Novel approach to segment skin lesions in dermoscopic images based on a deformable model","volume":"20","author":"Ma","year":"2016","journal-title":"IEEE J. Biomed. Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.eswa.2016.05.017","article-title":"A computational approach for detecting pigmented skin lesions in macroscopic images","volume":"61","author":"Oliveira","year":"2016","journal-title":"Expert. Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.cmpb.2017.07.009","article-title":"Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation","volume":"149","author":"Pennisi","year":"2017","journal-title":"Comput. Meth. Prog. Bio."},{"key":"ref_17","unstructured":"Rosado, L., Vasconcelos, M.J.V., Castro, R., and Tavares, J.M.R.S. (2015). From Dermoscopy to Mobile Teledermatology. Dermoscopy Image Analysis, CRC Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0262-8856(98)00091-2","article-title":"Segmentation of skin cancer images","volume":"17","author":"Xu","year":"1999","journal-title":"Image Vis. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/JSTSP.2008.2011119","article-title":"Comparison of segmentation methods for melanoma diagnosis in dermoscopy images","volume":"3","author":"Silveira","year":"2009","journal-title":"IEEE J. Sel. Top. Signal Process"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S0010-4825(97)00020-6","article-title":"DullRazor: A software approach to hair removal from images","volume":"27","author":"Lee","year":"1997","journal-title":"Comput. Biol. Med."},{"key":"ref_21","unstructured":"(2017, December 01). Available online: http:\/\/www.dermweb.com\/dull_razor."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wighton, P., Lee, T.K., and Atkinsa, M.S. (2008). Dermoscopic hair disocclusion using inpainting. Proc. SPIE Med. Imaging, 1\u20138.","DOI":"10.1117\/12.770776"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5486","DOI":"10.1109\/TIP.2014.2362054","article-title":"Hair enhancement in dermoscopic images using dual-channel quaternion tubularness filters and MRF-based multilabel optimization","volume":"23","author":"Mirzaalian","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","first-page":"559","article-title":"A pixel interpolation technique for curved hair removal in skin images to support melanoma detection","volume":"70","author":"Satheesha","year":"2014","journal-title":"J. Theor. App. Infor. Tech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.bspc.2011.01.003","article-title":"Hair removal methods: A comparative study for dermoscopy images","volume":"6","author":"Abbas","year":"2011","journal-title":"Biomed. Signal Proces."},{"key":"ref_26","unstructured":"(2017, December 01). Available online: https:\/\/uwaterloo.ca\/vision-image-processing-lab\/research-demos\/skin-cancer-detection."},{"key":"ref_27","unstructured":"(2017, December 01). Available online: https:\/\/challenge.kitware.com\/#challenge\/560d7856cad3a57cfde481ba."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chang, W.-Y., Huang, A., Yang, C.-Y., Lee, C.-H., Chen, Y.-C., Wu, T.-Y., and Chen, G.-S. (2013). Computer-aided diagnosis of skin lesions using conventional digital photography: A reliability and feasibility study. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0076212"},{"key":"ref_29","first-page":"318","article-title":"Multilevel image thresholding by nature-inspired algorithms: A short review","volume":"22","author":"Tuba","year":"2014","journal-title":"Comput. Sci. J. Mold."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Satapathy, S.C., Raja, N.S.M., Rajinikanth, V., Ashour, A.S., and Dey, N. (2016). Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput. Applic.","DOI":"10.1007\/s00521-016-2645-5"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1016\/j.procs.2015.02.064","article-title":"RGB histogram based color image segmentation using firefly algorithm","volume":"46","author":"Rajinikanth","year":"2015","journal-title":"Proced. Com. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Samanta, S., Acharjee, S., Mukherjee, A., Das, D., and Dey, N. (2013, January 26\u201328). Ant Weight lifting algorithm for image segmentation. Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India.","DOI":"10.1109\/ICCIC.2013.6724160"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/978-81-322-2755-7_40","article-title":"Robust color image multi-thresholding using between-class variance and cuckoo search algorithm","volume":"433","author":"Rajinikanth","year":"2016","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/978-81-322-2671-0_54","article-title":"Optimal multilevel image thresholding to improve the visibility of Plasmodium sp. in blood smear images","volume":"397","author":"Balan","year":"2016","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_35","first-page":"443","article-title":"Optimal multilevel image thresholding: An analysis with PSO and BFO algorithms","volume":"8","author":"Rajinikanth","year":"2014","journal-title":"Aust. J. Basic Appl. Sci."},{"key":"ref_36","first-page":"794574","article-title":"Otsu based optimal multilevel image thresholding using firefly algorithm","volume":"2014","author":"Raja","year":"2014","journal-title":"Model. Sim. Engg."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s40747-016-0022-8","article-title":"Social group optimization (SGO): A new population evolutionary optimization technique","volume":"2","author":"Satapathy","year":"2016","journal-title":"Complex Intell. Sys."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Naik, A., Satapathy, S.C., Ashour, A.S., and Dey, N. (2016). Social group optimization for global optimization of multimodal functions and data clustering problems. Neural Comput. Applic.","DOI":"10.1007\/s00521-016-2686-9"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dey, N., Ashour, A.S., and Althoupety, A.S. (2017). Thermal Imaging in Medical Science. Recent Advances in Applied Thermal Imaging for Industrial Applications, IGI.","DOI":"10.4018\/978-1-5225-5204-8.ch046"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Moraru, L., Moldovanu, S., Culea-Florescu, A.-L., Bibicu, D., Ashour, A.S., and Dey, N. (2017). Texture analysis of parasitological liver fibrosis images. Microsc. Res. Tech.","DOI":"10.1002\/jemt.22875"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1504\/IJES.2017.081720","article-title":"Effect of trigonometric functions-based watermarking on blood vessel extraction: An application in ophthalmology imaging","volume":"9","author":"Dey","year":"2017","journal-title":"Int. J. Embed. Sys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s11517-016-1508-7","article-title":"Effect of fuzzy partitioning in Crohn\u2019s disease classification: A neuro-fuzzy-based approach","volume":"55","author":"Ahmed","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1007\/s10916-016-0634-y","article-title":"Decision making based on fuzzy aggregation operators for medical diagnosis from dental x-ray images","volume":"40","author":"Ngan","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1166\/jamr.2016.1282","article-title":"Healthy and unhealthy rat hippocampus cells classification: A neural based automated system for alzheimer disease classification","volume":"11","author":"Dey","year":"2016","journal-title":"J. Adv. Microsc. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.cmpb.2015.10.023","article-title":"Measurement of glomerulus diameter and Bowman\u2019s space width of renal albino rats","volume":"126","author":"Kotyk","year":"2016","journal-title":"Comput. Meth. Prog. Bio."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1166\/jmihi.2016.1593","article-title":"Ensemble Clustering Algorithm with Supervised Classification of Clinical Data for Early Diagnosis of Coronary Artery Disease","volume":"6","author":"Kausar","year":"2016","journal-title":"J. Med. Imaging Health Inf."},{"key":"ref_48","first-page":"S1","article-title":"Automated Identification of Calcium Coronary Lesion Frames From Intravascular Ultrasound Videos","volume":"33","author":"Araki","year":"2014","journal-title":"J. Ultrasound Med."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0734-189X(85)90125-2","article-title":"A new method for gray-level picture thresholding using the entropy of the histogram","volume":"29","author":"Kapur","year":"1985","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_50","first-page":"51","article-title":"Chaotic cuckoo search and Kapur\/Tsallis approach in segmentation of T. cruzi from blood smear images","volume":"14","author":"Lakshmi","year":"2016","journal-title":"Int. J. Comp. Sci. Infor. Sec. (IJCSIS)"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Manic, K.S., Priya, R.K., and Rajinikanth, V. (2016). Image multithresholding based on Kapur\/Tsallis entropy and firefly algorithm. Ind. J. Sci. Technol., 9.","DOI":"10.17485\/ijst\/2016\/v9i12\/89949"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.1016\/j.asoc.2012.03.072","article-title":"A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding","volume":"13","author":"Akay","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1016\/j.eswa.2014.09.049","article-title":"Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur\u2019s, Otsu and Tsallis functions","volume":"42","author":"Bhandari","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF01385685","article-title":"A geometric model for active contours in image processing","volume":"66","author":"Caselles","year":"1993","journal-title":"Numer. Math."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/34.368173","article-title":"Shape modeling with front propagation: A level set approach","volume":"17","author":"Malladi","year":"1995","journal-title":"IEEE Trans. Pattern Anal. Mac. Int."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3243","DOI":"10.1109\/TIP.2010.2069690","article-title":"Distance regularized level set evolution and its application to image segmentation","volume":"19","author":"Li","year":"2010","journal-title":"IEEE T. Image Process."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1166\/jbic.2014.1080","article-title":"Geometrical analysis of schistosome egg images using distance regularized level set method forautomated species identification","volume":"3","author":"Vaishnavi","year":"2014","journal-title":"J. Bioinform. Intell. Cont."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10851-007-0002-0","article-title":"Fast global minimization of the active contour\/snake model","volume":"28","author":"Bresson","year":"2007","journal-title":"J. Math. Imaging Vis."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1109\/TIP.2008.2004611","article-title":"Localizing region-based active contours","volume":"17","author":"Lankton","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s40708-016-0033-7","article-title":"Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images","volume":"3","author":"Chaddad","year":"2016","journal-title":"Brain Infor."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/LSP.2003.821748","article-title":"Distance-reciprocal distortion measurefor binary document images","volume":"11","author":"Lu","year":"2004","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1016\/j.patcog.2009.12.024","article-title":"A multi-scale framework for adaptive binarization of degraded document images","volume":"43","author":"Moghaddam","year":"2010","journal-title":"Pat. Recognit."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Mostafa, A., Hassanien, A.E., Houseni, M., and Hefny, H. (2017). Liver segmentation in MRI images based on whale optimization algorithm. Multimed. Tools Appl.","DOI":"10.1007\/s11042-017-4638-5"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_66","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Demsar","year":"2006","journal-title":"J. Mach. Learn. Res."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/2\/51\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:55:54Z","timestamp":1760194554000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/2\/51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,22]]},"references-count":66,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["sym10020051"],"URL":"https:\/\/doi.org\/10.3390\/sym10020051","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,22]]}}}