Fuzzy model-based clustering and its application in image segmentation

Choy, Siu Kai; Lam, Benson S. Y.; Yu, Kwok Wai; Lee, Wing Yan; Leung, King Tai

doi:10.1016/j.patcog.2017.03.009

Cited by 89 publications

(24 citation statements)

References 54 publications

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“…Clustering is one of the major unsupervised learning techniques and has been applied in many fields such as pattern recognition [1], image processing [2,3], community detection [4,5], bioinformatics [6,7], information retrieval [8,9], and so on. e main task of clustering is to classify a dataset into some nonoverlapping clusters based on a suitable similarity metric so that the elements in the same cluster are similar, while any elements from different clusters are dissimilar.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang

She

2020

Mathematical Problems in Engineering

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The target of the clustering analysis is to group a set of data points into several clusters based on the similarity or distance. The similarity or distance is usually a scalar used in numerous traditional clustering algorithms. Nevertheless, a vector, such as data gravitational force, contains more information than a scalar and can be applied in clustering analysis to promote clustering performance. Therefore, this paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation. In the first stage, a sparse gravitational graph is constructed based on the top k data gravitations between each data point and its neighbors in the local region. Then the sparse graph is partitioned into many subgraphs by the gravitational influence coefficient. In the last stage, the satisfactory clustering result is obtained by merging these subgraphs iteratively by using a new linkage criterion. To demonstrate the performance of GHC algorithm, the experiments on synthetic and real-world data sets are conducted, and the results show that the GHC algorithm achieves better performance than the other existing clustering algorithms.

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Section: Introductionmentioning

confidence: 99%

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang

She

2020

Mathematical Problems in Engineering

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“…Image segmentation is an important aspect of image processing and determines the final results and quality of image analysis. Clustering-based image segmentation methods have been widely used [Choy, Shu, Yu et al (2017); Chen, Li, Bo (2017); Yang, Chung, Wang (2009)]. A clustering method uses the feature information of the image pixels to segment the image by merging pixels with identical or similar features into one group based on the similarity measure.…”

Section: Introductionmentioning

confidence: 99%

An Improved Unsupervised Image Segmentation Method Based on Multi-objective Particle Swarm Optimization Clustering Algorithm

Liu¹,

Bao²,

Song³

et al. 2019

Computers, Materials &Amp; Continua

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Most image segmentation methods based on clustering algorithms use singleobjective function to implement image segmentation. To avoid the defect, this paper proposes a new image segmentation method based on a multi-objective particle swarm optimization (PSO) clustering algorithm. This unsupervised algorithm not only offers a new similarity computing approach based on electromagnetic forces, but also obtains the proper number of clusters which is determined by scale-space theory. It is experimentally demonstrated that the applicability and effectiveness of the proposed multi-objective PSO clustering algorithm

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“…K-means clustering is the most representative model of hard clustering. Soft clustering can be further subdivided into mixture models and fuzzy c-means (FCM) [20], where FCM assumes pixels pertain to more than one class and has been widely studied and applied. However, it is worth noting that FCM does not take any spatial information into consideration [21].…”

Section: Introductionmentioning

confidence: 99%

A Robust Brain MRI Segmentation and Bias Field Correction Method Integrating Local Contextual Information into a Clustering Model

Zhang

Song

2019

Applied Sciences

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The segmentation results of brain magnetic resonance imaging (MRI) have important guiding significance for subsequent clinical diagnosis and treatment. However, brain MRI segmentation is a complex and challenging problem due to the inevitable noise or intensity inhomogeneity. A novel robust clustering with local contextual information (RC_LCI) model was used in this study which accurately segmented brain MRI corrupted by noise and intensity inhomogeneity. For pixels in the neighborhood of the central pixel, a weighting scheme combining local contextual information was used to generate the corresponding anisotropic weight to update the current central pixel and thus remove noisy pixels. Then, a multiplicative framework consisting of the product of a real image and a bias field could effectively segment brain MRI and estimate the bias field. Further, a linear combination of basis functions was introduced to guarantee the bias field properties. Therefore, compared with state-of-the-art models, the segmentation result obtained by RC_LCI was increased by 0.195 0.125 in terms of the Jaccard similarity coefficient. Both visual experimental results and quantitative evaluation demonstrate the superiority of RC_LCI on real and synthetic images.

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Fuzzy model-based clustering and its application in image segmentation

Cited by 89 publications

References 54 publications

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

An Improved Unsupervised Image Segmentation Method Based on Multi-objective Particle Swarm Optimization Clustering Algorithm

A Robust Brain MRI Segmentation and Bias Field Correction Method Integrating Local Contextual Information into a Clustering Model

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