基于自适应滤波的快速广义模糊C均值聚类图像分割

doi:10.16451/j.cnki.issn1003-6059.201811008

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Abstract When the fast generalized fuzzy C-means clustering(FGFCM]is directly used to segment serious noise images, the clustering center offset, inaccurate results and error of the image segmentation are easily caused due to the noise. Therefore, a fast generalized fuzzy C-means clustering algorithm based on adaptive filtering is proposed. Firstly, the parameter balance factor is adaptively determined according to the noise probability of nonlocal pixels to reflect the spatial structure information in the image more accurately. Then]the balance factor is used to effectively combine the linear weighted sum filtered image in the FGFCM algorithm with the median filtered image of the original image to create the adaptive filtered image. Since the filtering degree of the filtered image depends on the probability that the pixel is noise in the image, the dynamic noise suppression performance of the proposed method can be greatly improved. The experimental results show that compared with FCM and FGFCM, the proposed method obtains more accurate results in clustering segmentation of images with serious noise.

Key words： Fuzzy C-means Clustering Image Segmentation Adaptive Filtering Image Noise

Received: 30 August 2018

ZTFLH:

TP 391.4

Fund:Supported by National Natural Science Foundation of China(No.61761027)

Corresponding Authors: WANG Xiaopeng, Ph.D.professor. His research interests include image processing and analysis.

About author:: ZHANG Yongfang, master student. Her research interests include image analysis and understanding;WANG Wei, master student. His research interest include image processing;WEN Haotian, master student. His research interests include image analysis and understanding.

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Cite this article:

WANG Xiaopeng,ZHANG Yongfang,WANG Wei等. Image Segmentation Using Fast Generalized Fuzzy C-means Clustering Based on Adaptive Filtering[J]. , 2018, 31(11): 1040-1046.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201811008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I11/1040

[1]DUNN J C. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cyberne-tics, 1979, 3(3): 32-57.
[2]ADHIKARI S K, SING J K, BASU D K,et al.Conditional Spatial Fuzzy C-means Clustering Algorithm with Application in MRI Image Segmentation // MANDAL J, SATAPATHY S, KUMAR S M, et al., eds. Information Systems Design and Intelligent Applications. Berlin, Germany: Springer, 2015: 539-547.
[3]吴小燕,陈松灿.联机局部自适应模糊C均值聚类算法.模式识别与人工智能,2013, 26(11):1026-1032.
(WU X Y, CHEN S C. Online Local Adaptive Fuzzy C-means Clustering Algorithm. Pattern Recognition and Artificial Intelligence. 2013, 26(11):1026-1032.)
[4]ZHANG D Q, CHEN S C, PAN Z S, et al.Kernel-Based Fuzzy Clustering Incorporating Spatial Constraints for Image Segmentation // Proc of the 2nd International Conference on Machine Learning and Cybernetics. Washington, USA: IEEE, 2003: 2189-2192.
[5]李艳灵,沈轶.基于空间邻域信息的FCM图像分割算法.华中科技大学学报(自然科学版), 2009, 37(6): 56-59.
(LI Y L, SHEN Y. Fuzzy C-means Algorithm Based on the Spatial Information for Image Segmentation. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2009, 37(6):57-59.)
[6]GONG M G, LIANG Y, SHI J, et al. Fuzzy C-means Clustering with Local Information and Kernel Metric for Image Segmentation. IEEE Transactions on Image Processing, 2013, 22(2): 573-584.
[7]VERMA H, AGRAWAL R K]SHARAN A. An Improved Intuitionistic Fuzzy C-means Clustering Algorithm Incorporating Local Information for Brain Image Segmentation. Applied Soft Computing, 2016, 46: 543-557.
[8]LIU L, YANG A L, ZHOU W J, et al.]Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-means. IEEE/CAA Journal of Automatica Sinica, 2015, 2(3): 235-247.
[9]徐秋晔,李玉,林文杰,等.基于信息聚类的遥感图像分割.中国矿业大学学报, 2017, 46(1): 209-214.
(XU Q Y, LI Y, LIN W J, et al. Remote Sensing Image Segmentation Based on Information Clustering. Journal of China University of Mining and Technology, 2017, 46(1): 209-214.)
[10]HEMANTH D J, ANITHA J, BALAS V E. Fast and Accurate Fuzzy C-means Algorithm for MR Brain Image Segmentation. Inter-national Journal of Imaging Systems and Technology, 2016, 26(3):188-195.
[11]CONG W, SONG J H]WANG L, et al. A Fuzzy C-means Clus-tering]Scheme Incorporating Non-local Spatial Constraint for Brain Magnetic Resonance Image Segmentation. Journal of Medical Imaging and Health Informatics, 2015, 5(8): 1821-1825.
[12]KRINIDIS S, CHATZIS V. A Robust Fuzzy Local Information C-means Clustering Algorithm. IEEE Transactions on Image Processing, 2010, 19(5): 1328-1337.
[13]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. IEEE Transactions on Medical Imaging, 2002, 21(3): 193-199.
[14]CHEN S C, ZHANG D Q. Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-Induced Distance Measure. IEEE Transaction on Systems, Man, and Cybernetics(Cybernetics), 2004, 34(4): 1907-1916.
[15]SZILAGYI L, BENYO Z, SZILAGYI S M, et al. MR Brain Image Segmentation Using an Enhanced Fuzzy C-means Algorithm // Proc of the 25th Annual International Conference of the IEEE Enginee-ring]in Medicine and Biology Society. Washington, USA: IEEE, 2003: 17-21.
[16]CAI W L, CHEN S C, ZHANG D Q. Fast and Robust Fuzzy C-means Clustering Algorithms Incorporating Local Information for Image Segmentation. Pattern Recognition, 2007, 40(7): 825-838.
[17]ZHAO Z X, CHENG L Z, CHENG G Q. Neighbourhood Weighted Fuzzy C-means ClusteringAlgorithm for ImageSegmentation. IET Image Processing, 2014, 8(3): 150-161.