TY - JOUR
T1 - Sparse Regularization-Based Fuzzy C-Means Clustering Incorporating Morphological Grayscale Reconstruction and Wavelet Frames
AU - Wang, Cong
AU - Pedrycz, Witold
AU - Zhou, Mengchu
AU - Li, Zhiwu
N1 - Funding Information:
Manuscript received August 10, 2019; revised January 1, 2020 and February 17, 2020; accepted March 30, 2020. Date of publication April 14, 2020; date of current version July 1, 2021. This work was supported in part by the Doctoral Students’ Short Term Study Abroad Scholarship Fund of Xidian University, in part by the National Natural Science Foundation of China under Grant 61873342 and Grant 61672400, in part by the Recruitment Program of Global Experts, and in part by the Science and Technology Development Fund, MSAR, under Grant 0012/2019/A1. (Corresponding authors: MengChu Zhou; ZhiWu Li.) Cong Wang is with the School of Electro-Mechanical Engineering, Xidian University, Xi’an 710071, China (e-mail: wangc0705@stu.xidian.edu.cn).
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - The conventional fuzzy C-means (FCM) algorithm is not robust to noise and its rate of convergence is generally impacted by data distribution. Consequently, it is challenging to develop FCM-related algorithms that have good performance and require less computing time. In this article, we elaborate on a comprehensive FCM-related algorithm for image segmentation. To make FCM robust, we first utilize a morphological grayscale reconstruction (MGR) operation to filter observed images before clustering, which guarantees noise-immunity and image detail-preservation. Since real images can generally be approximated by sparse coefficients in a tight wavelet frame system, feature spaces of observed and filtered images can be obtained. Taking such features to be clustered, we investigate an improved FCM model in which a sparse regularization term is introduced into the objective function of FCM. We design a three-step iterative algorithm to solve the sparse regularization-based FCM model, which is constructed by the Lagrangian multiplier method, hard-threshold operator, and normalization operator, respectively. Such an algorithm can not only perform well for image segmentation, but also come with high computational efficiency. To further enhance the segmentation accuracy, we use MGR to filter the label set generated by clustering. Finally, a large number of supporting experiments and comparative studies with other FCM-related algorithms available in the literature are provided. The obtained results for synthetic, medical and color images indicate that the proposed algorithm has good ability for multiphase image segmentation, and performs better than other alternative FCM-related algorithms. Moreover, the proposed algorithm requires less time than most of the existing algorithms.
AB - The conventional fuzzy C-means (FCM) algorithm is not robust to noise and its rate of convergence is generally impacted by data distribution. Consequently, it is challenging to develop FCM-related algorithms that have good performance and require less computing time. In this article, we elaborate on a comprehensive FCM-related algorithm for image segmentation. To make FCM robust, we first utilize a morphological grayscale reconstruction (MGR) operation to filter observed images before clustering, which guarantees noise-immunity and image detail-preservation. Since real images can generally be approximated by sparse coefficients in a tight wavelet frame system, feature spaces of observed and filtered images can be obtained. Taking such features to be clustered, we investigate an improved FCM model in which a sparse regularization term is introduced into the objective function of FCM. We design a three-step iterative algorithm to solve the sparse regularization-based FCM model, which is constructed by the Lagrangian multiplier method, hard-threshold operator, and normalization operator, respectively. Such an algorithm can not only perform well for image segmentation, but also come with high computational efficiency. To further enhance the segmentation accuracy, we use MGR to filter the label set generated by clustering. Finally, a large number of supporting experiments and comparative studies with other FCM-related algorithms available in the literature are provided. The obtained results for synthetic, medical and color images indicate that the proposed algorithm has good ability for multiphase image segmentation, and performs better than other alternative FCM-related algorithms. Moreover, the proposed algorithm requires less time than most of the existing algorithms.
KW - Fuzzy C-means (FCM) algorithm
KW - Image segmentation
KW - Morphological grayscale reconstruction (MGR)
KW - Sparse regularization
KW - Tight wavelet frame
UR - http://www.scopus.com/inward/record.url?scp=85083693431&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083693431&partnerID=8YFLogxK
U2 - 10.1109/TFUZZ.2020.2985930
DO - 10.1109/TFUZZ.2020.2985930
M3 - Article
AN - SCOPUS:85083693431
SN - 1063-6706
VL - 29
SP - 1826
EP - 1840
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 7
M1 - 9067059
ER -