Robust semi-supervised spatial picture fuzzy clustering with local membership and KL-divergence for image segmentation

Wu, Chengmao; Zhang, Jia-Jia

doi:10.1007/s13042-021-01429-y

Cited by 13 publications

(2 citation statements)

References 59 publications

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“…In addition to the k-means and GMM methods, there are many other types of partitionbased clustering algorithms. For example, centroid-based algorithms like centroid-based algorithm with KL-divergence clustering [147], graph-based clustering algorithms with spectral clustering [148] and robust continuous clustering [149], and density-based algorithms, such as density-based spatial clustering of applications with a noise algorithm [150]. Each of these algorithms has its own advantages and disadvantages.…”

Section: Unsupervised Learning Methods For Data Processingmentioning

confidence: 99%

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Wang,

Fan,

Qiang

2023

Symmetry

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The design of aerospace systems is recognized as a complex interdisciplinary process. Many studies have shown that the exchange of information among multiple disciplines often results in strong coupling and nonlinearity characteristics in system optimization. Meanwhile, inevitable multi-source uncertainty factors continuously accumulate during the optimization process, greatly compromising the system’s robustness and reliability. In this context, uncertainty-based multidisciplinary design optimization (UMDO) has emerged and has been preliminarily applied in aerospace practices. However, it still encounters major challenges, including the complexity of multidisciplinary analysis modeling, and organizational and computational complexities of uncertainty analysis and optimization. Extensive research has been conducted recently to address these issues, particularly uncertainty analysis and artificial intelligence strategies. The former further enriches the UMDO technique, while the latter makes outstanding contributions to addressing the computational complexity of UMDO. With the aim of providing an overview of currently available methods, this paper summarizes existing state-of-the art UMDO technologies, with a special focus on relevant intelligent optimization strategies.

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Section: Unsupervised Learning Methods For Data Processingmentioning

confidence: 99%

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Wang,

Fan,

Qiang

2023

Symmetry

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“…The regions‐of‐interest were extracted using AMOSA, a simulated annealing‐based multiobjective optimization technique proposed by Bandyopadhyay et al (2008). Other examples of semi‐supervised clustering‐based methods include the contributions by Portela et al (2014), Yang et al (2020) and Wu and Zhang (2021). Although this approach enhances segmentation, quantifying the relatively marginal amount of labelled data is highly subjective.…”

Section: Related Workmentioning

confidence: 99%

Enhancement of clustering techniques by coupling clustering tree and neural network: Application to brain tumour segmentation

et al. 2022

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Currently, no classical clustering algorithm is efficient on its own. The predefined number of clusters required for their operation does not consistently produce satisfactory segmentation results. They exhibit cluster instability, are vulnerable to the local optimum trap, and are sensitive to noise and imaging artefacts. Most contributions designed to overcome these drawbacks incorporate prior knowledge such as cluster label information and statistic measures that demand minimal labelled training data. Although these approaches improve the segmentation accuracy, they tend to diminish the advantages of clustering algorithms over the supervised learning methods. This study proposes a shift from the use of a predefined number of clusters to a clustering tree-based method for performance enhancement of classical clustering algorithms. The proposed method is a three-stage algorithm. It begins with the extraction of low-level features from a clustering tree. Clustering trees are sets of labelled clusters of an image at multiple clustering resolutions. The second stage extracts high-level features by coupling the clustering tree to a single-layer feedforward neural network. The third stage is the classification stage, where the basic model of a neural network extracts the tumour from a high-level feature map.Because neither of the neural networks requires training, the proposed method is both fully unsupervised and fully automated and retains all its advantages over supervised methods. A performance evaluation using FLAIR MRI images of brain tumour patients from the BRATS2015 and BRATS2020 databases demonstrates significant performance enhancement over four classical clustering algorithms and two of the four proposed techniques were comparable to deep learning methods.

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Quadratic surface center-based possibilistic fuzzy clustering with kernel metric and local information for image segmentation

Wu,

Wang

2023

Multimed Tools Appl

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Robust semi-supervised spatial picture fuzzy clustering with local membership and KL-divergence for image segmentation

Cited by 13 publications

References 59 publications

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Enhancement of clustering techniques by coupling clustering tree and neural network: Application to brain tumour segmentation

Quadratic surface center-based possibilistic fuzzy clustering with kernel metric and local information for image segmentation

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