Segmentation of the Melanoma Lesion and its Border

ACS (2020). Key statistics for melanoma skin cancer, American Cancer Society, Atlanta, https://www.cancer.org/cancer/melanoma-skin-cancer/about/key-statistics.html. Search in Google Scholar

Agarwal, A., Issac, A. and Dutta, M. (2017). A region growing based imaging method for lesion segmentation from dermoscopic images, 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, Mathura, India, pp. 632–637. Search in Google Scholar

Ahn, E., Kim, J., Bi, L., Kumar, A., Li, C., Fulham, M. and Feng, D. (2017). Saliency-based lesion segmentation via background detection in dermoscopic images, Journal of Biomedical and Health Informatics 21(6): 1685–1693.10.1109/JBHI.2017.265317928092585 Search in Google Scholar

Ali, A.-R., Li, J., Yang, G. and O’Shea, S. (2020). A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images, PeerJ Computer Science 6: e268.10.7717/peerj-cs.268792446933816919 Search in Google Scholar

Aljanabi, M.,Özok, Y., Rahebi, J. and Abdullah, A. (2018). Skin lesion segmentation method for dermoscopy images using artificial bee colony algorithm, Symmetry 10(8): 347.10.3390/sym10080347 Search in Google Scholar

Ankerst, M., Breunig, M., Kriegel, H. and Sander, J. (1999). Optics: Ordering points to identify the clustering structure, ACM Sigmod Record 28(2): 49–60.10.1145/304181.304187 Search in Google Scholar

Ashour, A., Hawas, A., Guo, Y. and Wahba, M. (2018). A novel optimized neutrosophic k-means using genetic algorithm for skin lesion detection in dermoscopy images, Signal, Image and Video Processing 12(7): 1311–1318.10.1007/s11760-018-1284-y Search in Google Scholar

Bentley, J. (1975). Multidimensional binary search trees used for associative searching, Communications of the ACM 18(9): 509–517.10.1145/361002.361007 Search in Google Scholar

Bi, L., Kim, J., Ahn, E., Feng, D. and Fulham, M. (2016). Automated skin lesion segmentation via image-wise supervised learning and multi-scale superpixel based cellular automata, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, pp. 1059–1062. Search in Google Scholar

Bi, L., Kim, J., Ahn, E., Kumar, A., Fulham, M. and Feng, D. (2017). Dermoscopic image segmentation via multistage fully convolutional networks, IEEE Transactions on Biomedical Engineering 64(9): 2065–2074.10.1109/TBME.2017.271277128600236 Search in Google Scholar

Bozorgtabar, B., Abedini, M. and Garnavi, R. (2016). Sparse coding based skin lesion segmentation using dynamic rule-based refinement, International Workshop on Machine Learning in Medical Imaging, Athens, Greece, pp. 254–261. Search in Google Scholar

Campello, R., Moulavi, D. and Sander, J. (2013). Density-based clustering based on hierarchical density estimates, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, Australia, pp. 160–172. Search in Google Scholar

Celebi, E., Codella, N. and Halpern, A. (2019). Dermoscopy image analysis: Overview and future directions, Journal of Biomedical and Health Informatics 23(2): 474–478.10.1109/JBHI.2019.289580330703051 Search in Google Scholar

Celebi, E., Wen, Q., Hwang, S., Iyatomi, H. and Schaefer, G. (2013). Lesion border detection in dermoscopy images using ensembles of thresholding methods, Skin Research and Technology 19(1): e252–e258.10.1111/j.1600-0846.2012.00636.x22676490 Search in Google Scholar

Celebi, M., Kingravi, H. and Iyatomi, H. (2008). Border detection in dermoscopy images using statistical region merging, Skin Research and Technology 14(3): 347–353.10.1111/j.1600-0846.2008.00301.x316066919159382 Search in Google Scholar

Celebi, M., Kingravi, H. and Uddin, B. (2007). A methodological approach to the classification of dermoscopy images, Computerized Medical Imaging and Graphics 31(6): 362–373.10.1016/j.compmedimag.2007.01.003319240517387001 Search in Google Scholar

Celebi, M., Wen, Q., Iyatomi, H., Shimizu, K., Zhou, H. and Schaefer, G. (2015). A state-of-the-art survey on lesion border detection in dermoscopy images, Dermoscopy Image Analysis 10: 97–129. Search in Google Scholar

Codella, N., Rotemberg, V., Tschandl, P., Celebi, M., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., Kittler, H. and Halpern, A. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the International Skin Imaging Collaboration (ISIC), arXiv 1902.03368. Search in Google Scholar

De Vita, V., Di Leo, G., Fabbrocini, G., Liguori, C., Paolillo, A. and Sommella, P. (2012). Statistical techniques applied to the automatic diagnosis of dermoscopic images, Acta Imeko 1(1): 7–18.10.21014/acta_imeko.v1i1.7 Search in Google Scholar

Ester, M., Kriegel, H., Sander, J. and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, USA, pp. 226–231. Search in Google Scholar

Esteva, A., Kuprel, B., Novoa, R., Ko, J., Swetter, S., Blau, H. and Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks, Nature 542(7639): 115–118.10.1038/nature21056838223228117445 Search in Google Scholar

Ferreira, P., Mendonça, T. and Rocha, P. (2013). A wide spread of algorithms for automatic segmentation of dermoscopic images, Iberian Conference on Pattern Recognition and Image Analysis, Madeira, Portugal, pp. 592–599. Search in Google Scholar

Goyal, M., Oakley, A., Bansal, P., Dancey, D. and Yap, M. (2019). Skin lesion segmentation in dermoscopic images with ensemble deep learning methods, IEEE Access 8: 4171–4181.10.1109/ACCESS.2019.2960504 Search in Google Scholar

Guaragnella, C. and Rizzi, M. (2020). Simple and accurate border detection algorithm for melanoma computer aided diagnosis, Diagnostics 10(6): 423.10.3390/diagnostics10060423734440832580377 Search in Google Scholar

Hahsler, M. (2021). Density based clustering of applications with noise (DBSCAN) and related algorithms—R package, https://github.com/mhahsler/dbscan. Search in Google Scholar

Hahsler, M., Piekenbrock, M. and Doran, D. (2019). DBSCAN: Fast density-based clustering with R, Journal of Statistical Software 91: 1–30.10.18637/jss.v091.i01 Search in Google Scholar

Hinneburg, A. and Keim, D. A. (1998). An efficient approach to clustering in large multimedia databases with noise, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, USA, pp. 58–65. Search in Google Scholar

Hornik, K. (2021). The Comprehensive R Archive Network, https://cran.r-project.org. Search in Google Scholar

Indraswari, R., Herulambang, W. and Rokhana, R. (2017). Melanoma classification using automatic region growing for image segmentation, Proceeding of the International Conference on Technology and Applications, Surabaya, Indonesia, pp. 165–172. Search in Google Scholar

Jaworek-Korjakowska, J. and Tadeusiewicz, R. (2013). Hair removal from dermoscopic color images, Bio-Algorithms and Med-Systems 9(2): 53–58.10.1515/bams-2013-0013 Search in Google Scholar

Jensen, D. and Elewski, B. (2015). The ABCDEF rule: Combining the ‘ABCDE RULE’ and the ‘ugly duckling sign’ in an effort to improve patient self-screening examinations, Journal of Clinical and Aesthetic Dermatology 8(2): 15. Search in Google Scholar

Keefe, M., Dick, D. and Wakeel, R. (1990). A study of the value of the seven-point checklist in distinguishing benign pigmented lesions from melanoma, Clinical and Experimental Dermatology 15(3): 167–171.10.1111/j.1365-2230.1990.tb02064.x2142028 Search in Google Scholar

Khan, M., Akram, T., Sharif, M., Shahzad, A., Aurangzeb, K., Alhussein, M., Haider, S. and Altamrah, A. (2018). An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification, BMC Cancer 18(1): 1–20.10.1186/s12885-018-4465-8598943829871593 Search in Google Scholar

Kockara, S., Mete, M., Chen, B. and Aydin, K. (2010). Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images, BMC Bioinformatics 11(6): 1–11.10.1186/1471-2105-11-S6-S26302637320946610 Search in Google Scholar

Kroon, D. (2004). Region growing, https://www.mathworks.com/matlabcentral/fileexchange/19084-region-growing. Search in Google Scholar

Lee, T., Ng, V., Gallagher, R., Coldman, A. and McLean, D. (1997). DullRazor: A software approach to hair removal from images, Computers in Biology and Medicine 27(6): 533–543.10.1016/S0010-4825(97)00020-6 Search in Google Scholar

Louhichi, S., Gzara, M. and Abdallah, H. (2018). Skin lesion segmentation using multiple density clustering algorithm mdcut and region growing, IEEE/ACIS 17th International Conference on Computer and Information Science, Singapore, Singapore, pp. 74–79. Search in Google Scholar

Louhichi, S., Gzara, M. and Ben-Abdallah, H. (2017). Unsupervised varied density based clustering algorithm using spline, Pattern Recognition Letters 93: 48–57.10.1016/j.patrec.2016.10.014 Search in Google Scholar

Masood, A. and Al-Jumaily, A. (2013). Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms, International Journal of Biomedical Imaging 7: 323268.10.1155/2013/323268388522724575126 Search in Google Scholar

Melanoma ML (2018). Data set, https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:114463, DOI: 10.17026/dans-zue-zz2y. Open DOISearch in Google Scholar

Mendonça, T., Ferreira, P., Marques, J., Marcal, A. and Rozeira, J. (2013). Ph2—A dermoscopic image database for research and benchmarking, 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, pp. 5437–5440. Search in Google Scholar

Mete, M., Kockara, S. and Aydin, K. (2011). Fast density-based lesion detection in dermoscopy images, Computerized Medical Imaging and Graphics 35(2): 128–136.10.1016/j.compmedimag.2010.07.00720800995 Search in Google Scholar

Mete, M. and Sirakov, N. (2010). Lesion detection in dermoscopy images with novel density-based and active contour approaches, BMC Bioinformatics 11(6): 1–13.10.1186/1471-2105-11-S6-S23302637120946607 Search in Google Scholar

Mishra, N. and Celebi, M. (2016). An overview of melanoma detection in dermoscopy images using image processing and machine learning, arXiv 1601.07843. Search in Google Scholar

Møllersen, K., Kirchesch, H.M., Schopf, T.G. and Godtliebsen, F. (2010). Unsupervised segmentation for digital dermoscopic images, Skin Research and Technology 16(4): 401–407.10.1111/j.1600-0846.2010.00455.x20923456 Search in Google Scholar

Mount, D. and Arya, S. (2010). ANN: A library for approximate nearest neighbor searching, https://github.com/dials/annlib. Search in Google Scholar

Oliveira, R., Mercedes, F., Ma, Z., Papa, J., Pereira, A. and Tavares, J. (2016). Computational methods for the image segmentation of pigmented skin lesions: A review, Computer Methods and Programs in Biomedicine 131: 127–141.10.1016/j.cmpb.2016.03.03227265054 Search in Google Scholar

Oliveira, R., Papa, J. and Pereira, A. (2018). Computational methods for pigmented skin lesion classification in images: Review and future trends, Neural Computing and Applications 29(3): 613–636.10.1007/s00521-016-2482-6 Search in Google Scholar

Olugbara, O., Taiwo, T. and Heukelman, D. (2018). Segmentation of melanoma skin lesion using perceptual color difference saliency with morphological analysis, Mathematical Problems in Engineering 2018, Article ID: 1524286.10.1155/2018/1524286 Search in Google Scholar

Pathan, S., Prabhu, K. and Siddalingaswamy, P. (2018a). Hair detection and lesion segmentation in dermoscopic images using domain knowledge, Medical & Biological Engineering & Computing 56(11): 2051–2065.10.1007/s11517-018-1837-929761315 Search in Google Scholar

Pathan, S., Prabhu, K. and Siddalingaswamy, P. (2018b). Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—A review, Biomedical Signal Processing and Control 39: 237–262.10.1016/j.bspc.2017.07.010 Search in Google Scholar

Patiño, D., Avendaño, J. and Branch, J. (2018). Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging, International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain, pp. 728–736. Search in Google Scholar

Pennisi, A., Bloisi, D., Nardi, D., Giampetruzzi, A., Mondino, C. and Facchiano, A. (2016). Skin lesion image segmentation using Delaunay triangulation for melanoma detection, Computerized Medical Imaging and Graphics 52: 89–103.10.1016/j.compmedimag.2016.05.00227215953 Search in Google Scholar

Pradhan, R., Kumar, S., Agarwal, R., Pradhan, M.P. and Ghose, M. (2010). Contour line tracing algorithm for digital topographic maps, International Journal of Image Processing 4(2): 156–163. Search in Google Scholar

Rizzi, M. and Guaragnella, C. (2020). Skin lesion segmentation using image bit-plane multilayer approach, Applied Sciences 10(9): 3045.10.3390/app10093045 Search in Google Scholar

Sadeghi, M., Razmara, M., Lee, T. and Atkins, M. (2011). A novel method for detection of pigment network in dermoscopic images using graphs, Computerized Medical Imaging and Graphics 35(2): 137–143.10.1016/j.compmedimag.2010.07.00220724109 Search in Google Scholar

Smaoui, N. and Bessassi, S. (2013). Melanoma skin cancer detection based on region growing segmentation, International Journal of Computer Vision and Signal Processing 1(1): 1–7. Search in Google Scholar

Stanley, R., Stoecker, W. and Moss, R. (2007). A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images, Skin Research and Technology 13(1): 62–72.10.1111/j.1600-0846.2007.00192.x318488717250534 Search in Google Scholar

Suer, S., Kockara, S. and Mete, M. (2011). An improved border detection in dermoscopy images for density based clustering, BMC Bioinformatics 12(10): 1–10.10.1186/1471-2105-12-S10-S12323683422166058 Search in Google Scholar

Vestergaard, M., Macaskill, P., Holt, P. and Menzies, S. (2008). Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: A meta-analysis of studies performed in a clinical setting, British Journal of Dermatology 159(3): 669–676.10.1111/j.1365-2133.2008.08713.x18616769 Search in Google Scholar

Wang, H., Moss, R., Chen, X. and Stanley, R. (2011). Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images, Computerized Medical Imaging and Graphics 35(2): 116–120.10.1016/j.compmedimag.2010.09.006318357520970307 Search in Google Scholar

Zhou, H., Schaefer, G., Celebi, M., Lin, F. and Liu, T. (2011). Gradient vector flow with mean shift for skin lesion segmentation, Computerized Medical Imaging and Graphics 35(2): 121–127.10.1016/j.compmedimag.2010.08.00220832242 Search in Google Scholar