A novel deviation density peaks clustering algorithm and its applications of medical image segmentation

Wei, Zhou; Wang, Limin; Han, Xuming; Li, Mingyang

doi:10.1049/ipr2.12594

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…We demonstrated the effectiveness of the new adaptive method for outlier assignment on datasets with more complex density structures. We compared the clustering results of the proposed algorithm with the classical k-means [14], AP [15], DBSCAN [16], DPC [10], DPCSA [35], AmDPC [36], and DeDPC [37] algorithms for analysis. Among them, DPCSA, AmDPC and DeDPC are the most advanced clustering algorithms proposed in recent years.…”

Section: Resultsmentioning

confidence: 99%

Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm

Wei

Wang

Han

et al. 2023

Entropy

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The density-based spatial clustering of application with noise (DBSCAN) algorithm is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To solve the above problems, we propose an adaptive DBSCAN method based on the chameleon swarm algorithm (CSA-DBSCAN). First, we take the clustering evaluation index of the DBSCNA algorithm as the objective function and use the chameleon swarm algorithm (CSA) to iteratively optimize the evaluation index value of the DBSCAN algorithm to obtain the best Eps value and clustering result. Then, we introduce the theory of deviation in the data point spatial distance of the nearest neighbor search mechanism to assign the identified noise points, which solves the problem of over-identification of the algorithm noise points. Finally, we construct color image superpixel information to improve the CSA-DBSCAN algorithm’s performance regarding image segmentation. The simulation results of synthetic datasets, real-world datasets, and color images show that the CSA-DBSCAN algorithm can quickly find accurate clustering results and segment color images effectively. The CSA-DBSCAN algorithm has certain clustering effectiveness and practicality.

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Section: Resultsmentioning

confidence: 99%

Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm

Wei

Wang

Han

et al. 2023

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…It should be noted that the parameter setting of McDPC algorithm is relatively dependent on the nature of the dataset [18]. Learning from the experimental settings of the original paper [18] and other works [26,27], we set the search range of the parameters in this paper, as shown in Table 1. All the experiments were conducted in Matlab 2017B.…”

Section: Experiments and Resultsmentioning

confidence: 99%

ConDPC: Data Connectivity-Based Density Peak Clustering

Zou

Wang

2022

Applied Sciences

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As a relatively novel density-based clustering algorithm, Density peak clustering (DPC) has been widely studied in recent years. DPC sorts all points in descending order of local density and finds neighbors for each point in turn to assign all points to the appropriate clusters. The algorithm is simple and effective but has some limitations in applicable scenarios. If the density difference between clusters is large or the data distribution is in a nested structure, the clustering effect of this algorithm is poor. This study incorporates the idea of connectivity into the original algorithm and proposes an improved density peak clustering algorithm ConDPC. ConDPC modifies the strategy of obtaining clustering center points and assigning neighbors and improves the clustering accuracy of the original density peak clustering algorithm. In this study, clustering comparison experiments were conducted on synthetic data sets and real-world data sets. The compared algorithms include original DPC, DBSCAN, K-means and two improved algorithms over DPC. The comparison results prove the effectiveness of ConDPC.

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“…When dealing with low density points, it is di cult for the DPC algorithm to identify boundary points and outliers without a reasonable allocation strategy. Zhou et al [94] proposed a novel deviation density peaks clustering (DeDPC) algorithm in 2022.…”

Section: Applications Of Dpcmentioning

confidence: 99%

An overview on density peaks clustering

Wei

Peng

Huang

2023

Preprint

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Density Peaks Clustering (DPC) algorithm is a new algorithm based on density clustering analysis, which can quickly obtain the cluster centers by drawing the decision diagram by using the calculation of local density and relative distance. Without prior knowledge and iteration, the parameters and structure are simple and easy to implement. Since it was proposed in 2014, it has attracted a large number of researchers to explore experiments and improve applications in recent years. In this paper, we first analyze the theory of DPC and its performance advantages and disadvantages. Secondly, it summarizes the improvement of DPC in recent years, analyzes the improvement effect, and shows it with experimental data. Finally, the related application research of DPC in different fields is introduced. At the same time, we summarize and prospect the improvement and development of DPC.

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A novel deviation density peaks clustering algorithm and its applications of medical image segmentation

Cited by 6 publications

References 33 publications

Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm

Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm

ConDPC: Data Connectivity-Based Density Peak Clustering

An overview on density peaks clustering

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