{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:24:05Z","timestamp":1761953045677,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533978","type":"print"},{"value":"9789819533985","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3398-5_4","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:19:35Z","timestamp":1761952775000},"page":"39-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Martingale-Based Skin Lesion Segmentation from\u00a0Dermoscopic Images"],"prefix":"10.1007","author":[{"given":"Yao","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Ashwini, A., Sahila, T., Radhakrishnan, A., Vanitha, M., Loretta, G. I.: Automatic skin tumor detection in dermoscopic samples using online patch fuzzy region based segmentation. Biomed. Signal Process. Control 100, 107096 (2025)","DOI":"10.1016\/j.bspc.2024.107096"},{"key":"4_CR2","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/978-981-15-2780-7_87","volume-title":"Intelligent Computing in Engineering","author":"DNH Thanh","year":"2020","unstructured":"Thanh, D.N.H., Hien, N.N., Surya Prasath, V.B., Erkan, U., Khamparia, A.: Adaptive thresholding skin lesion segmentation with gabor filters and principal component analysis. In: Solanki, V.K., Hoang, M.K., Lu, Z.J., Pattnaik, P.K. (eds.) Intelligent Computing in Engineering. AISC, vol. 1125, pp. 811\u2013820. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-2780-7_87"},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cmpb.2018.11.001","volume":"168","author":"JL Garcia-Arroyo","year":"2019","unstructured":"Garcia-Arroyo, J.L., Garcia-Zapirain, B.: Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding. Comput. Methods Programs Biomed. 168, 11\u201319 (2019)","journal-title":"Comput. Methods Programs Biomed."},{"key":"4_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101924","volume":"59","author":"PMM Pereira","year":"2020","unstructured":"Pereira, P.M.M., et al.: Dermoscopic skin lesion image segmentation based on local binary pattern clustering: comparative study. Biomed. Signal Process. Control 59, 101924 (2020)","journal-title":"Biomed. Signal Process. Control"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Gopal, A., Alagarsamy, P.C., Kalivaradhan, U.M., Gandhimaruthian, L.: An automatic region based optimal segmentation and detection of features on dermoscopy images using V-shaped waterfall and water ridges. Traitement du Signal 40(2) (2023)","DOI":"10.18280\/ts.400210"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Fawzy, S., Moustafa, H.E.-D., Ata, M.M., AbdelHay, E.H.: High performed skin lesion segmentation based on modified active contour. In: 2022 International Telecommunications Conference (ITC-Egypt), pp. 1\u20135. IEEE (2022)","DOI":"10.1109\/ITC-Egypt55520.2022.9855766"},{"issue":"4","key":"4_CR7","doi-asserted-by":"publisher","DOI":"10.1088\/2631-8695\/ad9b00","volume":"6","author":"JC Kavitha","year":"2024","unstructured":"Kavitha, J.C., Subitha, D., Nagarajan, D.: Self-adaptive canny edge detection with reinforcement learning and dominant texture color patterns for melanoma segmentation and classification in dermoscopic images. Eng. Res. Exp. 6(4), 045254 (2024)","journal-title":"Eng. Res. Exp."},{"key":"4_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/978-3-030-05918-7_30","volume-title":"Mining Intelligence and Knowledge Exploration","author":"O Salih","year":"2018","unstructured":"Salih, O., Viriri, S.: Skin lesion segmentation using enhanced unified Markov random field. In: Groza, A., Prasath, R. (eds.) MIKE 2018. LNCS (LNAI), vol. 11308, pp. 331\u2013340. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-05918-7_30"},{"issue":"8","key":"4_CR9","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1049\/iet-cvi.2018.5289","volume":"12","author":"SM Jaisakthi","year":"2018","unstructured":"Jaisakthi, S.M., Mirunalini, P., Aravindan, C.: Automated skin lesion segmentation of dermoscopic images using GrabCut and k-means algorithms. IET Comput. Vision 12(8), 1088\u20131095 (2018)","journal-title":"IET Comput. Vision"},{"key":"4_CR10","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/978-3-030-04946-1_43","volume-title":"Cognitive Internet of Things: Frameworks, Tools and Applications","author":"K Hu","year":"2020","unstructured":"Hu, K., et al.: A skin lesion segmentation method based on saliency and adaptive thresholding in wavelet domain. In: Lu, H. (ed.) ISAIR 2018. SCI, vol. 810, pp. 445\u2013453. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-04946-1_43"},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.neucom.2021.10.017","volume":"468","author":"R Gu","year":"2022","unstructured":"Gu, R., Wang, L., Zhang, L.: DE-Net: a deep edge network with boundary information for automatic skin lesion segmentation. Neurocomputing 468, 71\u201384 (2022)","journal-title":"Neurocomputing"},{"issue":"11","key":"4_CR12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0277578","volume":"17","author":"Y Dong","year":"2022","unstructured":"Dong, Y., Wang, L., Li, Y.: TC-Net: dual coding network of transformer and CNN for skin lesion segmentation. PLoS ONE 17(11), e0277578 (2022)","journal-title":"PLoS ONE"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Doob, J.L.: What is a martingale? Am. Math. Mon. 78(5), 451\u2013463 (1971)","DOI":"10.1080\/00029890.1971.11992788"},{"key":"4_CR14","unstructured":"Vovk, V., et al.: Testing exchangeability on-line. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003), pp. 768\u2013775 (2003)"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Lu, G., et al.: A novel framework of change-point detection for machine monitoring. Mech. Syst. Signal Process. 83, 533\u2013548 (2017)","DOI":"10.1016\/j.ymssp.2016.06.030"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179\u2013187 (1962)","DOI":"10.1109\/TIT.1962.1057692"},{"key":"4_CR17","unstructured":"Gutman, D., et al.: 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 preprint arXiv:1605.01397 (2016)"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Esparza, E., et al.: An efficient Harris hawks-inspired image segmentation method. Expert Syst. Appl. 155, 113428 (2020)","DOI":"10.1016\/j.eswa.2020.113428"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Oskouei, A.G., et al.: CGFFCM: cluster-weight and group-local feature-weight learning in fuzzy C-means clustering algorithm for color image segmentation. Appl. Soft Comput. 113, 108005 (2021)","DOI":"10.1016\/j.asoc.2021.108005"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Wali, S., et al.: Level-set evolution for medical image segmentation with alternating direction method of multipliers. Signal Process. 211, 109105 (2023)","DOI":"10.1016\/j.sigpro.2023.109105"},{"key":"4_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-030-59716-0_25","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"M Brudfors","year":"2020","unstructured":"Brudfors, M., Balbastre, Y., Flandin, G., Nachev, P., Ashburner, J.: Flexible Bayesian modelling for nonlinear image registration. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 253\u2013263. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_25"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: Kullback\u2013Leibler divergence-based fuzzy C-means clustering incorporating morphological reconstruction and wavelet frames for image segmentation. IEEE Trans. Cybern. 52(8), 7612\u20137623 (2021)","DOI":"10.1109\/TCYB.2021.3099503"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Shang, R., et al.: SAR image segmentation using region smoothing and label correction. Remote Sens. 12(5), 803 (2020)","DOI":"10.3390\/rs12050803"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3398-5_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T23:19:37Z","timestamp":1761952777000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3398-5_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819533978","9789819533985"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3398-5_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that\u00a0are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xuzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icig.csig.org.cn\/2025\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}