基于欧氏空间距离的加强模糊C均值聚类方法
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摘要
针对利用加强的模糊C均值(En FCM)聚类算法进行图像分割时未利用图像空间信息,造成算法对椒盐噪声敏感、分割结果不准确的问题,提出了一种基于欧氏空间距离的聚类方法。将图像中邻域像素到中心像素欧氏空间距离的倒数作为权重与邻域像素加权,引入图像的邻域和空间信息;将所得结果与中心像素求和,对原始图像滤波;在图像的灰度直方图上进行聚类运算,得到分割结果。引入了欧氏空间距离的滤波函数,同时考虑了图像的邻域信息和空间信息,有效抑制了聚类过程中噪声的影响。实验结果表明:与En FCM算法相比,提出的方法对椒盐噪声鲁棒性更好,可获得更为理想的分割结果。
Using the enhanced fuzzy C-means( En FCM) clustering algorithm for image segmentation can greatly improve the efficiency of the algorithm. However,due to lack of using spatial information of the image,results in En FCM algorithm is more sensitive to the salt and pepper noise,and the segmentation results are not accurate. So an En FCM clustering method based on Euclidean space distance is proposed. The reciprocal of the Euclidean space distance of neighbor pixels and to central pixel in image acts as weight for weighing with neighbor pixel,to introduce spatial and neighbor information. Result is summed with the central pixel to filter the original image.Clustering operation is carried out on gray histogram of image,get the segmentation result. Filtering function of Euclidean space distance is introduced,not only consider neighborhood information,but also spatial information at the same time,and effectively suppress the influence of noise in the clustering process. The experimental results show that,comparing with En FCM algorithm,the proposed method is more robust to the salt and pepper noise and more ideal segmentation result can be obtained.
Using the enhanced fuzzy C-means( En FCM) clustering algorithm for image segmentation can greatly improve the efficiency of the algorithm. However,due to lack of using spatial information of the image,results in En FCM algorithm is more sensitive to the salt and pepper noise,and the segmentation results are not accurate. So an En FCM clustering method based on Euclidean space distance is proposed. The reciprocal of the Euclidean space distance of neighbor pixels and to central pixel in image acts as weight for weighing with neighbor pixel,to introduce spatial and neighbor information. Result is summed with the central pixel to filter the original image.Clustering operation is carried out on gray histogram of image,get the segmentation result. Filtering function of Euclidean space distance is introduced,not only consider neighborhood information,but also spatial information at the same time,and effectively suppress the influence of noise in the clustering process. The experimental results show that,comparing with En FCM algorithm,the proposed method is more robust to the salt and pepper noise and more ideal segmentation result can be obtained.
引文
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[14] Krinidis S,Chatzis V. A robust fuzzy local information C-means clustering algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging,2010,21(3):193-199.
[15] Szilagyi L,Benyo Z,Szilagyii S,et al. MR brain image segmentation using an enhanced fuzzy C-means algorithm[C]∥Proceedings of the 25th Annual International Conference of the IEEE EMBS,2003:17-21.
[2] Bezdek J. Pattern recognition with fuzzy objective function algorithms[M]. New York:Plenum,1981.
[3] Zhang D,Chen S. Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation[C]∥Proceedings of the 2nd International Conference on Machine Learning and Cybernetics,IEEE Conference Publication,2003:2189-2192.
[4]李艳灵,沈轶.基于空间邻域信息的FCM图像分割算法[J].华中科技大学学报:自然科学版,2009,37(6):57-59.
[5] Gong M,Liang Y,Shi J,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.
[6]赵泉华,李红莹,李玉.区域化模糊C均值高分辨率彩色遥感影像分割方法[J].控制与决策,2015,30(9):1706-1710.
[7] Liu L,Yang A L,Zhou W J. Robust dataset classification approach based on neighbor searching and kernel fuzzy C-means[J].IEEE/CAA Journal of Automatica Sinica,2015,2(3):235-247.
[8]安计勇,高贵阁,史志强,等.一种改进的K均值文本聚类算法[J].传感器与微系统,2015,34(5):130-133.
[9] Hemanth D J,Anitha J,Balas V E. Fast and accurate fuzzy Cmeans algorithm for MR brain image segmentation[J]. International Journal of Imaging Systems and Technology,2016,26(3):188-195.
[10]许芹,唐敦兵,蔡祺祥.改进的快速模糊C均值聚类图像分割算法[J].南京理工大学学报,2016,40(3):309-314.
[11]李文杰,廖晓纬,束仁义,等.基于Zig Bee和模糊C均值聚类方法在火警中的应用[J].传感器与微系统,2012,31(10):143-145,152.
[12] Ahmed M,Yamany S,Mohamed N,et al. A modified fuzzy Cmeans algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging,2002,21(3):193-199.
[13] Chen S,Zhang D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J].IEEE Transaction on Systems,Man,Cybernetics,Part B:Cybernetics,2004,34(4):1907-1916.
[14] Krinidis S,Chatzis V. A robust fuzzy local information C-means clustering algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging,2010,21(3):193-199.
[15] Szilagyi L,Benyo Z,Szilagyii S,et al. MR brain image segmentation using an enhanced fuzzy C-means algorithm[C]∥Proceedings of the 25th Annual International Conference of the IEEE EMBS,2003:17-21.
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