Fast Fuzzy C Means Algorithm for Segmentation of Non-destructive Testing Image

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Fast Fuzzy C Means Algorithm for Segmentation of Non-destructive Testing Image

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 06 Page number: 42-48 Column: Public date: 2022-09-02

Title:: Fast Fuzzy C Means Algorithm for Segmentation of Non-destructive Testing Image

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

Keywords:: fuzzy C means algorithm; image segmentation; non-destructive testing; robustness

CLC:: TP391. 4

DOI:: 10.13705/j.issn.1671-6833.2022.04.016

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.

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Last Update: 2022-10-03