Computer Science and Information Systems 2022 Volume 19, Issue 3, Pages: 1389-1408
https://doi.org/10.2298/CSIS220321039Y
Full text (
6158 KB)
Cited by
Adaptive wavelet transform based on artificial fish swarm optimization and fuzzy C-means method for noisy image segmentation
Yang Rui (School of Electronic and Electrical Engineering, Zhengzhou University of Science and Technology Zhengzhou, China + Henan Intelligent Information Processing and Control Engineering Technology Research Center Zhengzhou, China), [email protected]
Li Dahai (School of Electronic and Electrical Engineering, Zhengzhou University of Science and Technology Zhengzhou, China + Henan Intelligent Information Processing and Control Engineering Technology Research Center Zhengzhou, China), [email protected]
Aiming at the problem that traditional fuzzy C-means (FCM) clustering algorithm is susceptible to noise in processing noisy images, a noisy image segmentation method based on FCM wavelet domain feature enhancement is proposed. Firstly, the noise image is decomposed by two-dimensional wavelet. Secondly, the edge enhancement of the approximate coefficient is carried out, and the artificial fish swarm (AFS) optimization algorithm is used to process the threshold value of the detail coefficient, and the processed coefficient is reconstructed by wavelet transform. Finally, the reconstructed image is segmented by FCM algorithm. Five typical gray-scale images are selected by adding Gaussian noise and Salt& pepper noise, respectively, and segmented by various methods. The peak signal-to-noise ratio (PSNR) and error rate (MR) of segmented images are used as performance indexes. Experimental results show that compared with traditional FCM clustering algorithm segmentation method, particle swarm optimization (PSO) segmentation method and other methods, the indexes of image segmentation by the proposed method is greatly improved. It can be seen that the proposed segmentation method retains the texture information of image edge well, and its anti-noise performance and segmentation performance are improved.
Keywords: FCM, artificial fish swarm optimization, wavelet transform, noisy image segmentation
Show references
D. Fung, Q. Liu, J. Zammit, K. S. Leung and P. Hu. ”Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19,” Journal of Translational Medicine, vol. 19, no. 1. (2021)
Q.W. Shi, S. L. Yin, K.Wang, L. Teng and H. Li. ”Multichannel convolutional neural networkbased fuzzy active contour model for medical image segmentation,” Evolving Systems, (2021) https://doi.org/10.1007/s12530-021-09392-3
L. Duan, S. Yang, D. Zhang. ”Multilevel thresholding using an improved cuckoo search algorithm for image segmentation,” The Journal of Supercomputing, vol. 77, no. 7, pp. 6734-6753. (2021)
X. Xie , Q. Zhang. ”An edge-cloud-aided incremental tensor-based fuzzy c-means approach with big data fusion for exploring smart data,” Information Fusion, vol. 76, no. 5. (2021)
S. L. Yin, Y. Zhang, S. Karim. Large Scale Remote Sensing Image Segmentation Based on Fuzzy Region Competition and Gaussian Mixture Model,” IEEE Access, vol. 6, pp. 26069- 26080. (2018)
X. Lei and H. Ouyang. ”Kernel-Based Intuitionistic Fuzzy Clustering Image Segmentation Based on Grey Wolf Optimizer With Differential Mutation,” IEEE Access, vol. 9, pp. 85455- 85463. (2021)
L. Frigau, C. Conversano, F. Mola. ”Consistent validation of gray-level thresholding image segmentation algorithms based on machine learning classifiers,” Statistical Papers, vol. 62. (2021)
Z. Zhao, Z. Zeng, K. Xu, C. Chen and C. Guan. ”DSAL: Deeply Supervised Active Learning From Strong and Weak Labelers for Biomedical Image Segmentation,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 10, pp. 3744-3751. (2021)
X. Zheng, T. Chen. ”High spatial resolution remote sensing image segmentation based on the multiclassification model and the binary classification model,” Neural Computing and Applications, (2021).
G. An et al. ”Short-Term Wind Power Prediction Based On Particle Swarm Optimization- Extreme Learning Machine Model CombinedWith Adaboost Algorithm,” IEEE Access, vol. 9, pp. 94040-94052. (2021)
B. Chen, Y. Niu and H. Liu. ”Input-to-State Stabilization of Stochastic Markovian Jump Systems Under Communication Constraints: Genetic Algorithm-Based Performance Optimization,” IEEE Transactions on Cybernetics, (2021) doi: 10.1109/TCYB.2021.3066509.
A. Nd, B. Mk, C. Xl, et al. ”Towards robust partially supervised multi-structure medical image segmentation on small-scale data,” Applied Soft Computing, vol. 114. (2021)
H. Abdellahoum, N. Mokhtari, A. Br Ahimi, et al. ”CSFCM: An improved fuzzy C-Means image segmentation algorithm using a cooperative approach,” Expert Systems with Applications, 166:114063. (2021)
D. Chen, S. Yongchareon, E. Lai, J. Yu and Q. Z. Sheng. ”Hybrid Fuzzy C-Means CPD-Based Segmentation for Improving Sensor-Based Multiresident Activity Recognition,” IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11193-11207. (2021)
J. Fan and B. Lei. ”Image thresholding segmentation method based on reciprocal rough entropy,” Journal of Electronics & Information Technology, vol. 42, no. 1, pp. 214-221. (2020)
W. Seema, B. Keshavamurthy. ”A survey on image data analysis through clustering techniques for real world applications,” Journal of Visual Communication and Image Representation, vol. 55, pp. 596-626. (2018)
Z. Zhu, Y. Liu, Z. Zhao, et al. ”Improved suppressed fuzzy c-means clustering algorithm for segmenting the non-destructive testing image,” Chinese Journal of Scientific Instrument, vol. 40, no. 8, pp. 110-118. (2019)
D. Wei, Z. Wang, L. Si, et al. ”An image segmentation method based on a modified localinformation weighted intuitionistic Fuzzy C-means clustering and Gold-panning Algorithm,” Engineering Applications of Artificial Intelligence, vol. 101, no. 3, pp. 104209. (2021)
M. Kaushal, QMD Lohani. ”Generalized intuitionistic fuzzy c-means clustering algorithm using an adaptive intuitionistic fuzzification technique,” Granular Computing, vol. 1, pp. 183- 195. (2021)
Z. Yang, P. Xu, Y. Yang, et al. ”Noise robust intuitionistic fuzzy c-means clustering algorithm incorporating local information,” IET Image Processing, vol. 15, no. 3, pp. 805-813. (2021)
H. Liu, F. Zhao. ”Multiobjective fuzzy clustering with multiple spatial information for Noisy color image segmentation,” Applied Intelligence, vol. 5, pp. 5280-5298. (2021)
Y. Li and Y. Shen. ”Robust Image Segmentation Algorithm Using Fuzzy Clustering Based on Kernel-Induced Distance Measure,” 2008 International Conference on Computer Science and Software Engineering, 2008, pp. 1065-1068, doi: 10.1109/CSSE.2008.694.
H. Zhang, J. Liu. ”Fuzzy c-means clustering algorithm with deformable spatial information for image segmentation,” Multimedia Tools and Applications, vol. 81, no. 8, pp. 11239-11258. (2022)
X. Jia, T. Lei, X. Du, S. Liu, H. Meng and A. K. Nandi. ”Robust Self-Sparse Fuzzy Clustering for Image Segmentation,” IEEE Access, vol. 8, pp. 146182-146195, (2020).
W. Shi, J. Zhuo, Y. Lan, et al. ”A Novel Fuzzy Clustering Algorithm Based on Similarity of Attribute Space,” Journal of Electronics & Information Technology, vol. 41, no. 11, pp. 2722- 2728. (2019)
M. Gong, Y. Liang, J. Shi, et al. ”Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation,” IEEE Transactions on Image Processing, vol. 22, no. 2, pp. 573-584. (2013)
I. Cherfa, A. Mokraoui, A. Mekhmoukh and K. Mokrani. ”Adaptively Regularized Kernel- Based Fuzzy C-Means Clustering Algorithm Using Particle Swarm Optimization for Medical Image Segmentation,” 2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2020, pp. 24-29, doi: 10.23919/SPA50552.2020.9241242.
Memon, Kashif, Hussain, et al. ”Generalised kernel weighted fuzzy C-means clustering algorithm with local information,” Fuzzy Sets And Systems, vol. 340, no. 1, pp. 91-108. (2018)
H. B. Seidel, M. M. A. da Rosa, G. Paim, E. A. C. da Costa, S. J. M. Almeida and S. Bampi. ”Approximate Pruned and Truncated Haar Discrete Wavelet Transform VLSI Hardware for Energy-Efficient ECG Signal Processing,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 5, pp. 1814-1826. (2021)
A. Hy, B. Yang, C. Rw, et al. ”Associations between trees and grass presence with childhood asthma prevalence using deep learning image segmentation and a novel green view index,” Environmental Pollution, (2021)
A. Dz, A. Bh, G. Xun, et al. ”ASS-GAN:Asymmetric Semi-supervised GAN for Breast Ultrasound Image Segmentation,” Neurocomputing, (2022).
A. R. Beeravolu, S. Azam, M. Jonkman, B. Shanmugam, K. Kannoorpatti and A. Anwar. ”Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN,” IEEE Access, vol. 9, pp. 33438-33463. (2021)
Gao H, Jun-Wei Z. ”Estimation of pile settlement applying hybrid radial basis function network with BBO, ALO, and GWO optimization algorithms,” Journal of Applied Science and Engineering, vol. 25, no. 6, pp. 1031-1044, 2022.
Ibrahim, J., Gajin, S. ”Entropy-based Network Traffic Anomaly Classification Method Resilient to Deception,” Computer Science and Information Systems, Vol. 19, No. 1, pp. 87-116. (2022).
A. Skd, B. Kd et al. ”Opposition-based Laplacian Equilibrium Optimizer with Application in Image Segmentation using Multilevel Thresholding,” Expert Systems with Applications, vol. 174, (2021)
A. Joshi, M.S. Khan, A. Niaz, et al. ”Active Contour Model with Adaptive weighted function for Robust Image Segmentation under Biased Conditions,” Expert Systems with Applications, vol. 175, (2021).
J. Fang, H. Liu, J. Liu, et al. ”Fuzzy region-based active contour driven by global and local fitting energy for image segmentation,” Applied Soft Computing, vol. 100, (2021).
A. Srinivasan, S. Sadagopan. ”Rough fuzzy region based bounded support fuzzy C-means clustering for brain MR image segmentation,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 3775-3788, (2021).
C. L. Seng, B.A. Macdonald, M. Parsons, et al. ”Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm,” Journal of Real-Time Image Processing, vol. 11, pp. 1-16 (2021).