[1]王 卓,张长胜,钱俊兵.边缘细分的动态参数模糊C-均值图像分割算法[J].南京理工大学学报(自然科学版),2020,44(03):288-295.[doi:10.14177/j.cnki.32-1397n.2020.44.03.005 ]
 Wang Zhuo,Zhang Changsheng,Qian Junbing.Fuzzy C-mean image segmentation algorithm with dynamic parameters and edge subdivision[J].Journal of Nanjing University of Science and Technology,2020,44(03):288-295.[doi:10.14177/j.cnki.32-1397n.2020.44.03.005 ]
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边缘细分的动态参数模糊C-均值图像分割算法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年03期
页码:
288-295
栏目:
出版日期:
2020-06-30

文章信息/Info

Title:
Fuzzy C-mean image segmentation algorithm with dynamic parameters and edge subdivision
文章编号:
1005-9830(2020)03-0288-08
作者:
王 卓1张长胜1钱俊兵2
昆明理工大学 1.信息工程与自动化学院; 2.民航与航空学院,云南 昆明 650500
Author(s):
Wang Zhuo1Zhang Changsheng1Qian Junbing2
1.School of Information Engineering and Automation; 2.Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology,Kunming 650500,China
关键词:
边缘细分 动态参数 模糊C-均值 图像分割 局部灰度压缩 空间聚集度 滑动掩膜
Keywords:
fringe subdivision dynamic parameters fuzzy C-mean image segmentation local grayscale compression spatial aggregation degree sliding mask
分类号:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2020.44.03.005
摘要:
针对传统模糊C-均值(FCM)算法应用于图像分割时抗噪性差、分割精确度低等问题,该文提出一种边缘细分的动态参数模糊C-均值图像分割算法。对噪声图像进行局部灰度压缩并细分边缘像素信息,增强边界像素可分性。提出空间聚集度概念,更新像素隶属度,并设计滑动掩膜将像素细分为信息点、噪声点及边界点。根据像素类别引入动态参数,调整各自权重以增强算法自适应性。根据邻域像素聚类结果重新划分中心像素类别以提高算法聚类的容错率。采用3张图片进行算法性能测试,将该文算法实验结果与FCM_S1、FCM_S2和菱形邻域窗模糊C-均值(FCMD)算法实验结果进行对比。实验结果表明,该文算法除了对测试图像Cameraman的分割效果略显不足外,其余情况下的分割效果均优于其对比算法,划分系数Vpc可提高0.019 3~0.052 9,划分熵Vpe可降低0.026 9~0.094 4。
Abstract:
Aiming at the problems of poor noise resistance and low segmentation accuracy for traditional fuzzy C-mean(FCM)algorithm applied to image segmentation,a fuzzy C-mean image segmentation algorithm with dynamic parameters and edge subdivision is proposed here. An image is compressed by local grayscale and the edge pixel information is subdivided to enhance the edge pixel separability. The concept of spatial aggregation degree is proposed to update the membership degree of pixels,and a sliding mask is designed to subdivide pixels into information points,noise points and boundary points. According to the pixel category,dynamic parameters are introduced to adjust the weight of the pixel to enhance the self-adaptability of the algorithm. According to the result of neighborhood pixel clustering,the classification of center pixels is reclassified to improve the fault tolerance of the algorithm. Three pictures are used to test the performance of the algorithm,and the experimental results of the algorithm are compared with those of FCM-S1,FCM-S2 and fuzzy C-means of diamond(FCMD). Experimental results show that the segmentation effect of the proposed algorithm is better than those of the comparison algorithm except for the result of test image Cameraman. Partition coefficient Vpc can be improved by 0.019 3~0.052 9,and partition entropy Vpe can be reduced by 0.026 9~0.094 4.

参考文献/References:

[1] 杨蕴,李玉,王玉,等. 一种改进的Otsu多阈值SAR图像分割方法[J]. 遥感信息,2019,34(4):29-38.
Yang Yun,Li Yu,Wang Yu,et al. An improved Otsu multi-threshold SAR image segmentation method[J]. Remote Sensing Information,2019,34(4):29-38.
[2]Jin Ri,Weng Guirong. A robust active contour model driven by fuzzy C-means energy for fast image segmentation[J]. Digital Signal Processing,2019,90:100-109.
[3]董子昊,邵秀丽. 多类别的边缘感知方法在图像分割中的应用[J]. 计算机辅助设计与图形学学报,2019,31(7):1075-1085.
Dong Zihao,Shao Xiuli. A multi-category edge perception method for semantic segmentation[J]. Journal of Computer-Aided Design & Computer Graphics,2019,31(7):1075-1085.
[4]Huang Hong,Meng Fanzhi,Zhou Shaohua,et al. Brain image segmentation based on FCM clustering algorithm and rough set[J]. IEEE Access,2019,7:12386-12396.
[5]Sharma R P,Dey S. Two-stage quality adaptive fingerprint image enhancement using fuzzy C-means clustering based fingerprint quality analysis[J]. Image and Vision Computing,2019,83:1-16.
[6]Jiang Yizhang,Zhao Kaifa,Xia Kaijian,et al. A novel distributed multitask fuzzy clustering algorithm for automatic MR brain image segmentation[J]. Journal of Medical Systems,2019,43(5):118.
[7]Pham T,Siarry P,Oulhadj H. A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods[J]. Magnetic Resonance Imaging,2019,61:41-65.
[8]Li Muqing,Xu Luping,Gao Shan,et al. Adaptive segmentation of remote sensing images based on global spatial information[J]. Sensors,2019,19(10):2385.
[9]席亮,王勇,张凤斌. 基于自适应人工鱼群FCM的异常检测算法[J]. 计算机研究与发展,2019,56(5):1048-1059.
Xi Liang,Wang Yong,Zhang Fengbin. Anomaly detection algorithm based on FCM with adaptive artificial fish-swarm[J]. Journal of Computer Research and Development,2019,56(5):1048-1059.
[10]王展,杜平安,李杨,等. 基于FCM聚类的示温漆图像分割算法[J]. 航空动力学报,2018,33(3):604-610.
Wang Zhan,Du Pingan,Li Yang,et al. Segmentation algorithm for temperature indication paint image based on FCM clustering[J]. Journal of Aerospace Power,2018,33(3):604-610.
[11]兰蓉,林洋. 抑制式非局部空间直觉模糊C-均值图像分割算法[J]. 电子与信息学报,2019,41(6):1472-1479.
Lan Rong,Lin Yang. Suppressed non-local spatial intuitionistic fuzzy C-means image segmentation algorithm[J]. Journal of Electronics & Information Technology,2019,41(6):1472-1479.
[12]韩子硕,王春平. 基于改进FCM与MRF的SAR图像分割[J]. 系统工程与电子技术,2019,41(8):1726-1734.
Han Zishuo,Wang Chunping. SAR image segmentation based on improved FCM and MRF[J]. Systems Engineering and Electronics,2019,41(8):1726-1734.
[13]李磊,董卓莉,张德贤. 基于自适应区域限制FCM的图像分割方法[J]. 电子学报,2018,46(6):1312-1318.
Li lei,Dong Zhuoli,Zhang Dexian. Adaptive region constrained FCM algorithm for image segmentation[J]. Acta Electronica Sinica,2018,46(6):1312-1318.
[14]崔西希,吴成茂. 核空间中智模糊聚类及图像分割应用[J]. 中国图象图形学报,2016,21(10):1316-1327.
Cui Xixi,Wu Chengmao. Neutrosophic C-means clustering in kernel space and its application in image segmentation[J]. Journal of Image and Graphice,2016,21(10):1316-1327.
[15]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.
Xu Qin,Tang Dunbing,Cai Qixiang. Improved fast fuzzy C-means clustering algorithm for image segmentation[J]. Journal of Nanjing University of Science and Technology,2016,40(3):309-314.
[16]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.
[17]赵凤,刘汉强. 不同形状邻域空间信息的模糊聚类图像分割[J]. 计算机工程与应用,2015,51(10):12-15.
Zhao Feng,Liu Hanqiang. Fuzzy clustering image segmentation based on spatial information with different shape neighborhood[J]. Computer Engineering and Applications,2015,51(10):12-15.

备注/Memo

备注/Memo:
收稿日期:2019-10-03 修回日期:2020-03-30
基金项目:国家自然科学基金(51665025)
作者简介:王卓(1995-),男,硕士生,主要研究方向:优化算法、图像处理,E-mail:742245299@qq.com; 通讯作者:张长胜(1970-),男,副教授:主要研究方向:优化算法、图像处理,E-mail:13680377522qq.com。
引文格式:王卓,张长胜,钱俊兵. 边缘细分的动态参数模糊C-均值图像分割算法[J]. 南京理工大学学报,2020,44(3):288-295.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-06-30