Image Guided Fuzzy C-Means for Image Segmentation

Guo, Li; Chen, Long; Wu, Yingwen; Chen, C. L. Philip

doi:10.1007/s40815-017-0322-1

Cited by 18 publications

(5 citation statements)

References 30 publications

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“…Lu et al 37 proposed particle swarm fusion with FCM, and it used the particle swarm optimization algorithm to update the clustering center of FCM to obtain the global optimal value. Guo et al 38 took the input image as the guidance prior and proposed the image-guided FCM for image segmentation algorithm; it has the capability in noise suppression and edge-preserving smoothing. However, the iterative convergence of the algorithm is slow.…”

Section: Related Workmentioning

confidence: 99%

Infrared image segmentation based on multi-information fused fuzzy clustering method for electrical equipment

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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Serious noise pollution and background interference bring great difficulties to infrared image segmentation of electronic equipment. A novel infrared image segmentation method based on multi-information fused fuzzy clustering method is proposed in this article. Firstly, saliency detection is performed on the infrared image to obtain the saliency map, which determines the initial clustering center and enhances the contrast of the original infrared image. Secondly, the weighting exponent in the objective function is adjusted adaptively. Then local and global spatial constraints are added to the objective function of the fuzzy clustering method, which can reduce the noise and background interference. Finally, the Markov constrained field is calculated according to the initial segmentation result. After that the joint field of fuzzy clustering field and the Markov random field is constructed to obtain the optimized segmentation result. The algorithm is evaluated on the infrared images of electrical equipment, and the experimental results show that the proposed method is robust to noise and complicated background. Compared with other methods, the proposed method improves the average segmentation accuracy and T measure by about 10% and 13%.

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Section: Related Workmentioning

confidence: 99%

Infrared image segmentation based on multi-information fused fuzzy clustering method for electrical equipment

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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“…Recently in [28] Guo et al present a novel fuzzy clustering algorithm to segment brain MRI by adding a new term to the objective function of FCM. The new term comes from guided filter for its capability in noise suppression and edge‐preserving smoothing.…”

Section: Fcm Clustering Approachesmentioning

confidence: 99%

Fuzzy farthest point first method for MRI brain image clustering

Salem

Djemal

et al. 2019

IET image process

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Image clustering is considered amongst the most important tasks in medical image analysis and it is regularly required as a starter and vital stage in the computer‐aided medical image process. In brain magnetic resonance imaging (MRI) analysis, image clustering is regularly used for estimating and visualising the brain anatomical structures, to detect pathological regions and to guide surgical procedures. This study presents a new method for MRI brain images clustering based on the farthest point first algorithm and fuzzy clustering techniques without using any a priori information about the clusters number. The algorithm has been approved against both simulated and clinical magnetic resonance images and it has been compared with the fourth clustered algorithms. Results demonstrate that the proposed algorithm has given reasonable segmentation of white matter, grey matter and cerebrospinal fluid from MRI data, which is superior in preserving image details and segmentation accuracy compared with the other four algorithms giving more than 91% in Jaccard similarity.

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“…Then, the guided filter is considered to address this issue, which not only implements a smoothing filter on “flat patch” regions to relieve the impacts of noise but also ensures edge preserving on “high variance” regions [ 14 , 15 ]. The image-guided FCM (IGFCM) method is exploited by adding a guided filter to the optimization of FCM [ 16 ], but the complexity of the IGFCM model leads to vast calculation time. To tackle this problem, a new method called FCM + GF is further developed in [ 17 ], in which the FCM is employed to segment the raw noise image and the guided filter is implemented on the membership metrics by adopting the raw noise image as the guidance.…”

Section: Introductionmentioning

confidence: 99%

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

Qin

Zhou

et al. 2021

Journal of Healthcare Engineering

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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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Image Guided Fuzzy C-Means for Image Segmentation

Cited by 18 publications

References 30 publications

Infrared image segmentation based on multi-information fused fuzzy clustering method for electrical equipment

Infrared image segmentation based on multi-information fused fuzzy clustering method for electrical equipment

Fuzzy farthest point first method for MRI brain image clustering

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

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