{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T16:09:04Z","timestamp":1776096544985,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,18]],"date-time":"2018-04-18T00:00:00Z","timestamp":1524009600000},"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>This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. The images are described via three memberships: true, indeterminate, and false memberships. An indeterminate filter is then defined in the neutrosophic set for reducing the indeterminacy of the images. A neutrosophic c-means clustering algorithm is applied to segment the dermoscopic images. With the clustering results, skin lesions are identified precisely using an adaptive region growing method. To evaluate the performance of this algorithm, a public data set (ISIC 2017) is employed to train and test the proposed method. Fifty images are randomly selected for training and 500 images for testing. Several metrics are measured for quantitatively evaluating the performance of NCARG. The results establish that the proposed approach has the ability to detect a lesion with high accuracy, 95.3% average value, compared to the obtained average accuracy, 80.6%, found when employing the neutrosophic similarity score and level set (NSSLS) segmentation approach.<\/jats:p>","DOI":"10.3390\/sym10040119","type":"journal-article","created":{"date-parts":[[2018,4,19]],"date-time":"2018-04-19T10:12:01Z","timestamp":1524132721000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1814-9682","authenticated-orcid":false,"given":"Yanhui","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Illinois at Springfield, Springfield, IL 62703, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3217-6185","authenticated-orcid":false,"given":"Amira S.","family":"Ashour","sequence":"additional","affiliation":[{"name":"Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta 31527, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-5926","authenticated-orcid":false,"given":"Florentin","family":"Smarandache","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of New Mexico, 705 Gurley Ave., Gallup, NM 87301, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1016\/S0190-9622(03)02470-8","article-title":"Instruments and new technologies for the in vivo diagnosis of melanoma","volume":"49","author":"Marghoob","year":"2003","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1037\/0096-1523.29.2.483","article-title":"Changing your mind: On the contributions of top-down and bottom-up guidance in visual search for feature singletons","volume":"29","author":"Wolfe","year":"2003","journal-title":"J. Exp. Psychol. Hum. Percept. Perform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1001\/archderm.1995.01690150050011","article-title":"Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists","volume":"131","author":"Binder","year":"1995","journal-title":"Arch. Dermatol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Celebi, M.E., Mendonca, T., and Marques, J.S. (2015). A State-of-the-Art Survey on Lesion Border Detection in Dermoscopy Images. Dermoscopy Image Analysis, CRC Press.","DOI":"10.1201\/b19107"},{"key":"ref_5","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. Imaging Graph."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.compmedimag.2010.08.001","article-title":"Border detection in dermoscopy images using hybrid thresholding on optimized color channels","volume":"35","author":"Garnavi","year":"2011","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1111\/j.1600-0846.2007.00251.x","article-title":"Unsupervised border detection in dermoscopy images","volume":"13","author":"Celebi","year":"2007","journal-title":"Skin Res. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.1365-2133.2009.09416.x","article-title":"Slow-growing melanoma: A dermoscopy follow-up study","volume":"162","author":"Argenziano","year":"2010","journal-title":"Br. J. Dermatol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/j.patcog.2012.08.012","article-title":"Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm","volume":"46","author":"Xie","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.patcog.2017.04.023","article-title":"Development of a clinically oriented system for melanoma diagnosis","volume":"69","author":"Barata","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s10489-013-0474-0","article-title":"Skin cancer extraction with optimum fuzzy thresholding technique","volume":"40","author":"Lee","year":"2014","journal-title":"Appl. Intell."},{"key":"ref_13","unstructured":"Jaisakthi, S.M., Chandrabose, A., and Mirunalini, P. (arXiv, 2017). Automatic Skin Lesion Segmentation using Semi-supervised Learning Technique, arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/JSTSP.2008.2010631","article-title":"Anisotropic mean shift based fuzzy c-means segmentation of dermoscopy images","volume":"3","author":"Zhou","year":"2009","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2710","DOI":"10.1016\/j.patcog.2015.02.018","article-title":"NCM: Neutrosophic c-means clustering algorithm","volume":"48","author":"Guo","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mohan, J., Krishnaveni, V., and Guo, Y. (2015, January 26\u201327). Automated Brain Tumor Segmentation on MR Images Based on Neutrosophic Set Approach. Proceedings of the 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India.","DOI":"10.1109\/ECS.2015.7124747"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1016\/j.cviu.2011.04.001","article-title":"Color texture image segmentation based on neutrosophic set and wavelet transformation","volume":"115","author":"Sengur","year":"2011","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1186\/s40064-016-3211-4","article-title":"Segmentation of skin lesion using Cohen\u2013Daubechies\u2013Feauveau biorthogonal wavelet","volume":"5","author":"Khalid","year":"2016","journal-title":"SpringerPlus"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1137\/060649781","article-title":"Optimally Sparse Multidimensional Representation using Shearlets","volume":"39","author":"Guo","year":"2007","journal-title":"SIAM J. Math. Anal."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1137\/080741537","article-title":"Characterization and analysis of edges using the continuous shearlet transform","volume":"2","author":"Guo","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.compmedimag.2011.02.007","article-title":"Automated prescreening of pigmented skin lesions using standard cameras","volume":"35","author":"Cavalcanti","year":"2011","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_22","first-page":"254","article-title":"Sparse Multidimensional Representation Using Shearlets","volume":"Volume 5914","author":"Labate","year":"2005","journal-title":"Proceedings of the Wavelets XI"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, H., Niu, X., Qin, H., Zhou, J., Lai, R., and Wang, B. (2012, January 26\u201329). Shearlet Transform Based Anomaly Detection for Hyperspectral Image. Proceedings of the 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing, Imaging, and Solar Energy, Xiamen, China.","DOI":"10.1117\/12.978636"},{"key":"ref_24","unstructured":"Theresa, M.M. (2017). Computer aided diagnostic (CAD) for feature extraction of lungs in chest radiograph using different transform features. Biomed. Res., S208\u2013S213. Available online: http:\/\/www.biomedres.info\/biomedical-research\/computer-aided-diagnostic-cad-for-feature-extraction-of-lungs-in-chest-radiograph-using-different-transform-features.html."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.aeue.2013.12.003","article-title":"Image fusion based on shearlet transform and regional features","volume":"68","author":"Liu","year":"2014","journal-title":"AEU Int. J. Electron. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"177","DOI":"10.2478\/msr-2013-0027","article-title":"A new neutrosophic approach of Wiener filtering for MRI denoising","volume":"13","author":"Mohan","year":"2013","journal-title":"Meas. Sci. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mohan, J., Guo, Y., Krishnaveni, V., and Jeganathan, K. (2012, January 16\u201317). MRI Denoising Based on Neutrosophic Wiener Filtering. Proceedings of the 2012 IEEE International Conference on Imaging Systems and Techniques, Manchester, UK.","DOI":"10.1109\/IST.2012.6295518"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1142\/S1793005711001858","article-title":"A novel image segmentation approach based on neutrosophic set and improved fuzzy c-means algorithm","volume":"7","author":"Cheng","year":"2011","journal-title":"New Math. Nat. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1007\/s00521-016-2441-2","article-title":"A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering","volume":"28","author":"Guo","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhou, C., Chan, H.P., Chughtai, A., Wei, J., Hadjiiski, L.M., and Kazerooni, E.A. (2013). Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography. Med. Phys., 40.","DOI":"10.1118\/1.4812679"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.cmpb.2015.09.007","article-title":"A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set","volume":"123","author":"Guo","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_32","unstructured":"(2018, March 26). ISIC. Available online: http:\/\/www.isdis.net\/index.php\/isic-project."},{"key":"ref_33","unstructured":"Gutman, D., Codella, N.C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., and Halpern, A. (arXiv, 2016). 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), arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TMI.2016.2642839","article-title":"Automated melanoma recognition in dermoscopy images via very deep residual networks","volume":"36","author":"Yu","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_35","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/JBHI.2015.2390032","article-title":"A 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 Inform."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., and Kittler, H. (arXiv, 2017). Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), 2017, hosted by the international skin imaging collaboration (ISIC), arXiv.","DOI":"10.1109\/ISBI.2018.8363547"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/4\/119\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:01:12Z","timestamp":1760194872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/4\/119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,18]]},"references-count":38,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["sym10040119"],"URL":"https:\/\/doi.org\/10.3390\/sym10040119","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,18]]}}}