Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational...

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Vydané v:	IEEE transactions on fuzzy systems Ročník 26; číslo 5; s. 3027 - 3041
Hlavní autori:	Lei, Tao, Jia, Xiaohong, Zhang, Yanning, He, Lifeng, Meng, Hongying, Nandi, Asoke K.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Clustering Clustering algorithms Computational complexity Electronic mail Filtration Fuzzy c-means clustering (FCM) Image filters Image reconstruction Image segmentation Immunity Iterative methods Linear programming local spatial information Matrix partitioning morphological reconstruction (MR) Morphology Noise sensitivity Partitioning algorithms Pixels Robustness Spatial data State of the art
ISSN:	1063-6706, 1941-0034
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership partition. Compared with state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership partition matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.
AbstractList	As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership partition. Compared with state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership partition matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.
Author	Meng, Hongying Lei, Tao Nandi, Asoke K. He, Lifeng Zhang, Yanning Jia, Xiaohong
Author_xml	– sequence: 1 givenname: Tao orcidid: 0000-0002-2104-9298 surname: Lei fullname: Lei, Tao email: leitao@sust.edu.cn organization: College of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 2 givenname: Xiaohong surname: Jia fullname: Jia, Xiaohong email: jiaxhsust@163.com organization: College of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 3 givenname: Yanning surname: Zhang fullname: Zhang, Yanning email: ynzhang@nwpu.edu.cn organization: School of Computer Science, Northwestern Polytechnical University, Xi’an, China – sequence: 4 givenname: Lifeng surname: He fullname: He, Lifeng email: helifeng@ist.aichi-pu.ac.jp organization: College of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 5 givenname: Hongying orcidid: 0000-0002-8836-1382 surname: Meng fullname: Meng, Hongying email: hongying.meng@brunel.ac.uk organization: Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, U.K – sequence: 6 givenname: Asoke K. orcidid: 0000-0001-6248-2875 surname: Nandi fullname: Nandi, Asoke K. email: asoke.nandi@brunel.ac.uk organization: Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, U.K
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SubjectTerms	Algorithms Clustering Clustering algorithms Computational complexity Electronic mail Filtration Fuzzy c-means clustering (FCM) Image filters Image reconstruction Image segmentation Immunity Iterative methods Linear programming local spatial information Matrix partitioning morphological reconstruction (MR) Morphology Noise sensitivity Partitioning algorithms Pixels Robustness Spatial data State of the art
Title	Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering
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