IF 0.8 4区 工程技术Q3 ENGINEERING, MULTIDISCIPLINARYDynaPub Date : 2024-01-01DOI:10.6036/10787
Celia RAMOS PALENCIA, Dante Mújica Vargas, Jean Marie Vianney KINANI KINANI, Antonio LUNA ALVAREZ, Noé Alejandro Castro Sánchez
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引用次数: 0
摘要
本文提出了一种直观模糊聚类算法,该算法对皮肤镜图像中出现的非典型数据具有鲁棒性,并能划定受影响的区域。该算法从成员更新的目标函数推导出发,整合了一个 m 递减估计影响函数。实验结果表明,与其他聚类算法相比,该建议算法的划定准确率达到 95%,此外,迭代次数也大大减少:鲁棒直觉模糊聚类、皮肤镜图像、病变划界、M-降序估计器
INTUITIONISTIC ROBUST CLUSTERING FOR SEGMENTATION OF LESIONS IN DERMATOSCOPIC IMAGES
This paper presents the formulation of the intuitive fuzzy clustering algorithm to be robust to atypical data present in dermoscopic images and to delimit the affected area. This algorithm is formulated from the objective function derivation for memberships update, to integrate an m-redescending estimator influence function. Experimentation shows an accuracy of 95% with the proposal algorithm with respect to other clustering algorithms to perform delimitations, in addition the iterations number is considerably reduced.
Keywords: Robust Intuitionistic Fuzzy Clustering, Dermoscopic Images, Delimitations of Lesions, M-redescending Estimator
期刊介绍:
Founded in 1926, DYNA is one of the journal of general engineering most influential and prestigious in the world, as it recognizes Clarivate Analytics.
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Scientific journal agreed with AEIM (Spanish Association of Mechanical Engineering)
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