[1]王军芬,刘培跃,董建彬,等.用于分割无损检测图像的快速模糊C-均值算法[J].郑州大学学报(工学版),2022,43(06):42-48.[doi:10.13705/j.issn.1671-6833.2022.04.016]
　WANG Junfen,LIU Peiyue,DONG Jianbin,et al.Fast Fuzzy C Means Algorithm for Segmentation of Non-destructive Testing Image[J].Journal of Zhengzhou University (Engineering Science),2022,43(06):42-48.[doi:10.13705/j.issn.1671-6833.2022.04.016]
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用于分割无损检测图像的快速模糊C-均值算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
43
期数:
2022年06期
页码:
42-48
栏目:
出版日期:
2022-09-02

文章信息/Info

Title:
Fast Fuzzy C Means Algorithm for Segmentation of Non-destructive Testing Image
作者:
王军芬1; 2 ;  刘培跃3 ;  董建彬1; 2 ;  朱占龙1
1.河北地质大学信息工程学院;2.河北地质大学智能传感物联网技术河北省工程研究中心;3.石家庄职业技术学院机电工程系;

Author(s):
WANG Junfen1; 2;  LIU Peiyue3;  DONG Jianbin1;  2;  ZHU Zhanlong1
1.School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China; 
2.Intelligent Sensor Network Engineering Research Center of Hebei Province, Hebei GEO University, Shijiazhuang 050031, China; 
3.Department of Electromechanics Engineering, Shijiazhuang Vocational Technology Institute, Shijiazhuang 050081, China
关键词:
Keywords:
fuzzy C means algorithm;  image segmentation;  non-destructive testing;  robustness
分类号:
TP391. 4
DOI:
10.13705/j.issn.1671-6833.2022.04.016
文献标志码:
A
摘要:
无损检测图像中目标类和背景类差异较大,模糊 C 均值算法无法有效地将目标分割出来,因此提出一种用于分割无损检测图像的快速模糊 C 均值算法。 在聚类过程中,引入局部空间信息和灰度信息,以提高算法的鲁棒性;用条件值表征样本容量来平衡不同大小的类群,以解决类大小敏感问题;基于新的约束条件得到新的隶属度和聚类中心表达式,并给出算法具体步骤;对预处理后图像的灰度级进行分类,图像分割所需要的时间不再取决于图像的尺寸,而是图像的灰度级数,大幅度降低了算法的时间复杂度。 采用类大小差异较大的合成图像和无损检测图像进行仿真实验,以分割精度 ( SA) 、F-value、 G-mean 以及图像分割所需要的时间为评价指标来评价算法的性能。 实验结果表明:在原始测试图像被高斯噪声、椒盐噪声、瑞利噪声和乘性噪声污染时,与其他模糊聚类算法相比,本文算法具有更好的鲁棒性,分 割 精 度 更 高, 为 97. 93%, F-value 为 88. 50%, G-mean 为 93. 83%, 图 像 分 割 时 间 也 更 少, 为14. 06 m s。 实验证明了本文 算法的有 效性。
Abstract:
The Fuzzy C means algorithm cannot effectively segment object pixels from a non-destructive testing (NDT) image because of the great difference between the background and the object region. Therefore, a fast fuzzy C means algorithm for segmentation of NDT image was proposed in this study. In the process of clustering, local spatial information and gray information were introduced to improve the robustness of the algorithm. The condition value was used to represent the sample size to balance clusters of different sizes and solve the cluster-size sensitivity problem. Based on the new constraints, a new form of membership degree and cluster center representation could be obtained. The execution time of image segmentation was no longer determined by the size of the image, but by the gray level of the image. The computational complexity of the algorithm was greatly reduced. NDT images and synthetic images with a large difference in cluster size were used for testing. The segmentation accuracy (SA), F-value, G-mean and the execution time of image segmentation were used to evaluate the performance of algorithms. The experimental results showed that when the original test image was polluted by Gaussian noise, salt and pepper noise, Rayleigh noise and multiplicative noise, the proposed algorithm had better robustness, SA is 97.93%, F-value is 88.50%, G-mean is 93.83%, the execution time of image segmentation was less, about 14.06 ms. The simulation tests could verify the effectiveness of the proposed algorithm.

参考文献/References:

[1] 刘今越, 李顺达, 陈梦倩, 等. 面向移乘搬运护理 机器人的人体姿态视觉识 别 [ J] . 机 器 人, 2019, 41(5) : 601-608. 

LIU J Y, LI S D, CHEN M Q, et al. Visual recognition of human pose for the transfer-care assistant robot [ J] . Robot, 2019, 41(5) : 601-608. 
[2] 潘沛克, 王艳, 罗勇, 等. 基于 U-net 模型的全自 动鼻咽肿瘤 MR 图像分割[ J] . 计算机应用, 2019, 39(4) : 1183-1188. 
PAN P K, WANG Y, LUO Y, et al. Automatic segmentation of nasopharyngeal neoplasm in MR image based on U-net model[ J] . Journal of computer applications, 2019, 39(4) : 1183-1188.
 [3] 彭金柱, 董梦超, 杨扬. 基于视觉和肌电信息融合 的手势识 别 方 法 [ J] . 郑 州 大 学 学 报 ( 工 学 版) , 2021, 42(2) : 67-73. 
PENG J Z, DONG M C, YANG Y. Human gesture recognition method based on vision and EMG signal information[ J] . Journal of Zhengzhou university ( engineering science) , 2021, 42(2) : 67-73. 
[4] BEZDEK J C. A convergence theorem for the fuzzy ISODATA clustering algorithms[ J] . IEEE transactions on pattern analysis and machine intelligence, 1980, 2 (1) : 1-8.
 [5] BENSAID A M, HALL L O, BEZDEK J C, et al. Partially supervised clustering for image segmentation [ J] . Pattern recognition, 1996, 29(5) : 859-871. 
[6] CAI W L, CHEN S C, ZHANG D Q. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[ J] . Pattern recognition, 2007, 40(3) : 825-838. 
[7] ZHAO F, JIAO L C, LIU H Q, et al. A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation[ J] . Signal processing, 2011, 91(4) : 988-999. 
[8] GONG M G, 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.
 [9] MEMON K H, LEE D H. Generalised kernel weighted fuzzy C-means clustering algorithm with local information [ J ] . Fuzzy sets and systems, 2018, 340: 91 -108. 
[10] LEI T, JIA X H, ZHANG Y N, et al. Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering [ J ] . IEEE transactions on fuzzy systems, 2018, 26(5) : 3027-3041. 
[11] ZHANG Y X, BAI X Z, FAN R R, et al. Deviationsparse fuzzy c-means with neighbor information constraint[ J] . IEEE transactions on fuzzy systems, 2019, 27(1) : 185-199. 
[12] NOORDAM J C, Van Den BROEK W H A M, BUY-DENS L M C. Multivariate image segmentation with cluster size insensitive fuzzy c-means[ J] . Chemometrics and intelligent laboratory systems, 2002, 64( 1) : 65-78.
 [13] LIN P L, HUANG P W, KUO C H, et al. A sizeinsensitive integrity-based fuzzy c-means method for data clustering [ J ] . Pattern recognition, 2014, 47 (5) : 2042-2056. 
[14] LIANG J Y, BAI L, DANG C Y, et al. The K-meanstype algorithms versus imbalanced data distributions [ J] . IEEE transactions on fuzzy systems, 2012, 20 (4) : 728-745. 
[15] HAN H, WANG W Y, MAO B H. BorderlineSMOTE: a new over-sampling method in imbalanced data sets learning[ J] . Lecture notes in computer science, 2005, 3644:878-887.
 [16] SU C T, CHEN L S, YIH Y. Knowledge acquisition through information granulation for imbalanced data [ J] . Expert systems with applications, 2006, 31(3) : 531-541.

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更新日期/Last Update: 2022-10-03