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Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor

The guided filter is a novel explicit image filtering method, which implements a smoothing filter on “flat patch” regions and ensures edge preserving on “high variance” regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the cluste...

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Autores principales: Qin, Qingxue, Xu, Guangmei, Zhou, Jin, Wang, Rongrong, Jiang, Hui, Wang, Lin, Han, Shiyuan, Du, Tao, Ji, Ke, Zhao, Ya-ou, Zhang, Kun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455220/
https://www.ncbi.nlm.nih.gov/pubmed/34557289
http://dx.doi.org/10.1155/2021/6747371
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author Qin, Qingxue
Xu, Guangmei
Zhou, Jin
Wang, Rongrong
Jiang, Hui
Wang, Lin
Han, Shiyuan
Du, Tao
Ji, Ke
Zhao, Ya-ou
Zhang, Kun
author_facet Qin, Qingxue
Xu, Guangmei
Zhou, Jin
Wang, Rongrong
Jiang, Hui
Wang, Lin
Han, Shiyuan
Du, Tao
Ji, Ke
Zhao, Ya-ou
Zhang, Kun
author_sort Qin, Qingxue
collection PubMed
description The guided filter is a novel explicit image filtering method, which implements a smoothing filter on “flat patch” regions and ensures edge preserving on “high variance” regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the clustering results of noisy images. However, the adaptability of the existing guided filter-based FCM methods to different images is deteriorated, as the factor ε of the guided filter is fixed to a scalar. To solve this issue, this paper proposes a new guided filter-based FCM method (IFCM_GF), in which the guidance image of the guided filter is adjusted by a newly defined influence factor ρ. By dynamically changing the impact factor ρ, the IFCM_GF acquires excellent segmentation results on various noisy images. Furthermore, to promote the segmentation accuracy of images with heavy noise and simplify the selection of the influence factor ρ, we further propose a morphological reconstruction-based improved FCM clustering algorithm with guided filter (MRIFCM_GF). In this approach, the original noisy image is reconstructed by the morphological reconstruction (MR) before clustering, and the IFCM_GF is performed on the reconstructed image by utilizing the adjusted guidance image. Due to the efficiency of the MR to remove noise, the MRIFCM_GF achieves better segmentation results than the IFCM_GF on images with heavy noise and the selection of the influence factor for the MRIFCM_GF is simple. Experiments demonstrate the effectiveness of the presented methods.
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spelling pubmed-84552202021-09-22 Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor Qin, Qingxue Xu, Guangmei Zhou, Jin Wang, Rongrong Jiang, Hui Wang, Lin Han, Shiyuan Du, Tao Ji, Ke Zhao, Ya-ou Zhang, Kun J Healthc Eng Research Article The guided filter is a novel explicit image filtering method, which implements a smoothing filter on “flat patch” regions and ensures edge preserving on “high variance” regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the clustering results of noisy images. However, the adaptability of the existing guided filter-based FCM methods to different images is deteriorated, as the factor ε of the guided filter is fixed to a scalar. To solve this issue, this paper proposes a new guided filter-based FCM method (IFCM_GF), in which the guidance image of the guided filter is adjusted by a newly defined influence factor ρ. By dynamically changing the impact factor ρ, the IFCM_GF acquires excellent segmentation results on various noisy images. Furthermore, to promote the segmentation accuracy of images with heavy noise and simplify the selection of the influence factor ρ, we further propose a morphological reconstruction-based improved FCM clustering algorithm with guided filter (MRIFCM_GF). In this approach, the original noisy image is reconstructed by the morphological reconstruction (MR) before clustering, and the IFCM_GF is performed on the reconstructed image by utilizing the adjusted guidance image. Due to the efficiency of the MR to remove noise, the MRIFCM_GF achieves better segmentation results than the IFCM_GF on images with heavy noise and the selection of the influence factor for the MRIFCM_GF is simple. Experiments demonstrate the effectiveness of the presented methods. Hindawi 2021-09-13 /pmc/articles/PMC8455220/ /pubmed/34557289 http://dx.doi.org/10.1155/2021/6747371 Text en Copyright © 2021 Qingxue Qin et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Qin, Qingxue
Xu, Guangmei
Zhou, Jin
Wang, Rongrong
Jiang, Hui
Wang, Lin
Han, Shiyuan
Du, Tao
Ji, Ke
Zhao, Ya-ou
Zhang, Kun
Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor
title Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor
title_full Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor
title_fullStr Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor
title_full_unstemmed Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor
title_short Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor
title_sort morphological reconstruction-based image-guided fuzzy clustering with a novel impact factor
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455220/
https://www.ncbi.nlm.nih.gov/pubmed/34557289
http://dx.doi.org/10.1155/2021/6747371
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