[1]狄岚,刘海涛,何锐波.一种融合邻域信息的模糊C-均值图像分割算法[J].智能系统学报,2019,14(2):273-280.[doi:10.11992/tis.201712035]
DI Lan,LIU Haitao,HE Ruibo.Fuzzy C-means image segmentation algorithm incorporating neighborhood information[J].CAAI Transactions on Intelligent Systems,2019,14(2):273-280.[doi:10.11992/tis.201712035]
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DI Lan,LIU Haitao,HE Ruibo.Fuzzy C-means image segmentation algorithm incorporating neighborhood information[J].CAAI Transactions on Intelligent Systems,2019,14(2):273-280.[doi:10.11992/tis.201712035]
一种融合邻域信息的模糊C-均值图像分割算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第2期
页码:
273-280
栏目:
学术论文—机器学习
出版日期:
2019-03-05
- Title:
- Fuzzy C-means image segmentation algorithm incorporating neighborhood information
- Keywords:
- fuzzy C-means; image segmentation; spatial information; local information; non-local information; multidimensional model; neighborhood membership degree; penalty term
- 分类号:
- TP391.4
- 摘要:
- 模糊C-均值算法（fuzzy C-means，FCM）对图像噪声敏感，只考虑了图像数值信息而忽略了邻域空间信息，造成最终的图像分割结果不精确。为了克服FCM存在的问题，将图像局部信息与非局部信息融入到多测度模型中，扩充了原本聚类的单一测度。另外将先验概率引入隶属度矩阵中，使得每次迭代前，隶属度矩阵中像素点的邻域信息都被充分考虑，最后添加一个邻域隶属度惩罚项修正聚类结果。实验证明：该算法对噪声鲁棒性强，能够获得较为理想的图像分割效果。
- Abstract:
- The fuzzy C-means algorithm (FCM) is sensitive to image noise; in addition, it only considers the image numerical information and ignores the neighborhood spatial information, resulting in inaccurate final image segmentation result. To overcome this drawback, an FCM image segmentation algorithm is proposed in which the local information and non-local information of the image are integrated into a multidimensional model, which extends the original single dimension of clustering. In addition, a prior probability is introduced into the membership matrix, so that the neighborhood information of the pixel in the membership matrix is fully considered before each iteration, and then a neighborhood membership penalty is added to correct the clustering result. Finally, a penalty of neighborhood membership degree is used to modify the clustering results. Experimental results demonstrate that the algorithm is robust against noise and achieves an ideal image segmentation effect.
- 参考文献/References:
-
[1] HARALOCK R M, SHAPIRO L G. Computer and robot vision[M]. Boston:Addison-Wesley Longman Publishing Co., Inc., 1991.
[2] LI Nan, HUO Hong, ZHAO Yuming, et al. A spatial clustering method with edge weighting for image segmentation[J]. IEEE geoscience and remote sensing letters, 2013, 10(5):1124-1128.
[3] BARADEZ M O, MCGUKIN C P, FORRAZ N, et al. Robust and automated unimodal histogram thresholding and potential applications[J]. Pattern recognition, 2004, 37(6):1131-1148.
[4] 赵凤, 范九伦, 潘晓英, 等. 基于灰度和非局部空间灰度特征的二维Otsu曲线阈值分割法[J]. 计算机应用研究, 2012, 29(5):1987-1989, 2000 ZHAO Feng, FAN Jiulun, PAN Xiaoying, et al. Two-dimensional Otsu’s curve thresholding segmentation method based on gray and non local spatial gray feature[J]. Application research of computers, 2012, 29(5):1987-1989, 2000
[5] 代沁伶, 王雷光, 洪亮. 融合边缘测度的自然场景彩色图像区域分割[J]. 中国图象图形学报, 2013, 18(5):523-528 DAI Qinling, WANG Leiguang, HONG Liang. Regional segmentation of natural scene color images by integrating edge cues[J]. Journal of image and graphics, 2013, 18(5):523-528
[6] VINCENT L, SOILLE P. Watersheds in digital spaces:an efficient algorithm based on immersion simulations[J]. IEEE transactions on pattern analysis and machine intelligence, 1991, 13(6):583-598.
[7] CHOUDHRY M S, KAPOOR R. Performance analysis of fuzzy C-means clustering methods for MRI image segmentation[J]. Procedia computer science, 2016, 89:749-758.
[8] KRINIDIS S, CHATZIS V. A robust fuzzy local information C-means clustering algorithm[J]. IEEE transactions on image processing, 2010, 19(5):1328-1337.
[9] 王昭, 娄昊, 范九伦. 一种基于模糊C均值的图像超像素分割方法[J]. 微电子学与计算机, 2014(10):18-21, 27. WANG Zhao, LOU Hao, FAN Jiulun. Fuzzy C-means clustering algorithm with superpixel for image segmentation[J]. Microelectronics and computer, 2014(10):18-21, 27.
[10] 孙权森, 纪则轩. 基于模糊聚类的脑磁共振图像分割算法综述[J]. 数据采集与处理, 2016, 31(1):28-42 SUN Quansen, JI Zexuan. Fuzzy clustering for brain MR image segmentation[J]. Journal of data acquisition & processing, 2016, 31(1):28-42
[11] DUNN J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of cybernetics, 1973, 3(3):32-57.
[12] AHMED M N, YAMANY S M, MOHAMED N, et al. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE transactions on medical imaging, 2002, 21(3):193-199.
[13] CHEN Songcan, ZHANG Daoqiang. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE transactions on systems, man and cybernetics, part B (cybernetics), 2004, 34(4):1907-1916.
[14] Do M N, VETTERLI M. Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance[J]. IEEE transactions on image processing, 2002, 11(2):146-158.
[15] CAI Weiliang, CHEN Songcan, ZHANG Daoqiang. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern recognition, 2007, 40(3):825-838.
[16] YANG Y, ZHENG C, LIN P. Fuzzy c-means clustering algorithm with a novel penalty term for image segmentation[J]. Opto-electronics review, 2005, 13(4):309.
[17] GONG Maoguo, LIANG Yan, SHI Jiao, et al. Fuzzy C-means clustering with local information and kernel metric for image segmentation[J]. IEEE transactions on image processing, 2013, 22(2):573-584.
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备注/Memo
收稿日期:2017-12-25。
作者简介:狄岚，女，1965年生，副教授,主要研究方向为模式识别和数字图像处理。授权国家发明专利20多项。发表学术论文40余篇。;刘海涛，男，1991年生，硕士研究生，主要研究方向为图像处理。;何锐波，男，1994年生，硕士研究生，主要研究方向为图像处理和机器学习。
通讯作者:刘海涛.E-mail:382419872@qq.com
更新日期/Last Update:
2019-04-25