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Title: Residual-sparse fuzzy C-Means clustering incorporating morphological reconstruction and wavelet frames
Authors: Wang, Cong
Pedrycz, Witold
Li, Zhiwu
Zhou, Mengchu
Zhao, Jun
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Wang, C., Pedrycz, W., Li, Z., Zhou, M. & Zhao, J. (2020). Residual-sparse fuzzy C-Means clustering incorporating morphological reconstruction and wavelet frames. IEEE Transactions On Fuzzy Systems, 29(12), 3910-3924.
Journal: IEEE Transactions on Fuzzy Systems 
Abstract: In this article, we develop a residual-sparse Fuzzy C-Means (FCM) algorithm for image segmentation, which furthers FCM's robustness by realizing the favorable estimation of the residual (e.g., unknown noise) between an observed image and its ideal version (noise-free image). To achieve a sound tradeoff between detail preservation and noise suppression, morphological reconstruction is used to filter the observed image. By combining the observed and filtered images, a weighted sum image is generated. Tight wavelet frame decomposition is used to transform the weighted sum image into its corresponding feature set. Taking such feature set as data for clustering, we impose an ell _0 regularization term on residual to FCM's objective function, thus resulting in residual-sparse FCM, where spatial information is introduced for improving its robustness and making residual estimation more reliable. To further enhance segmentation accuracy of the proposed FCM, we employ morphological reconstruction to smoothen the labels generated by clustering. Finally, based on the prototypes and smoothed labels, a segmented image is reconstructed by using tight wavelet frame reconstruction. Experimental results regarding synthetic, medical, and real-world images show that the proposed algorithm is effective and efficient, and outperforms its peers.
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2020.3029296
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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