[1]ZHU Zhanlong,MA Yanling,DONG Jianbin,et al.Gray level-based fuzzy C-means algorithm for image segmentation with inhibitors of cluster contribution[J].CAAI Transactions on Intelligent Systems,2021,16(4):641-648.[doi:10.11992/tis.202009019]
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Gray level-based fuzzy C-means algorithm for image segmentation with inhibitors of cluster contribution

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 4 Page number: 641-648 Column: 学术论文—机器学习 Public date: 2021-07-05
Title:
Gray level-based fuzzy C-means algorithm for image segmentation with inhibitors of cluster contribution
Author(s):
ZHU Zhanlong1; 2; 3;  MA Yanling1; 2; 3;  DONG Jianbin1; 2; 3;  ZHENG Yibo2; 3
1. School of Information Engineering, Heibei GEO University, Shijiazhuang 050031, China;
2. Hebei Key Laboratory of Optoelectronic Information and Geo-Detection Technology, Shijiazhuang 050031, China;
3. Intelligent Sensor Network Engineering Research Center of Hebei Province, Shijiazhuang 050031, China
Keywords:
fuzzy C-means algorithm; image segmentation; gray levels; spatial information; non-destructive testing image; denoising; cluster center; objective function
CLC:
TP391
DOI:
10.11992/tis.202009019
Abstract:
The fuzzy C-means algorithm for image segmentation based on the gray level has the advantage of fast segmentation speed. However, this algorithm cannot effectively segment object pixels from a nondestructive testing (NDT) image because of the great difference between the background and the object region. Therefore, an improved fuzzy C-means algorithm based on gray level is proposed. First, an expression called inhibitors of cluster contribution, which is inversely related to the class size, is constructed. Incorporating this expression into the objective function reduces the contribution of the larger cluster to the objective function, which then avoids the influence of the larger cluster on the cluster center of the smaller cluster. Second, a new form of membership degree and cluster center representation can be obtained by minimizing the new objective function. Lastly, NDT images with a large difference in cluster size are used for testing. Results show that the segmentation image obtained by this algorithm has a better visual effect and the index G_mean is higher, thereby further improving the adaptability of the fuzzy C-means algorithm based on gray level.
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