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Segmentation of breast molybdenum target image lesions based on semi-supervised fuzzy clustering

JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2023年第6期44卷 9475-9493页

作者： Peng, Peng Wu, Danping Han, Fei-Chi Huang, Li-Jun Wei, Zhenlin Wang, Jie Jiang, Yizhang Xia, Kaijian Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China Soochow Univ Changshu Hosp Changshu Jiangsu Peoples R China Univ Idaho Coll Engn Moscow ID USA Eighth Peoples Hosp Nantong Jiangsu Peoples R China

Currently, breast cancer is one of the most common cancers among women. To aid clinicians in diagnosis, lesion regions in mammography pictures can be segmented using an artificial intelligence system. This has significant clinical implications. clustering algorithms, as unsupervised models, are widely used in medical image segmentation. However, due to the different sizes and shapes of lesions in mammography images and the low contrast between lesion areas and the surrounding pixels, it is difficult to use traditional unsupervised clustering methods for image segmentation. In this study, we try to apply the semisupervised fuzzy clustering algorithm to lesion segmentation in mammography molybdenum target images and propose semisupervised fuzzy clustering based on the cluster centres of labelled samples (called SFCM_V, where V stands for cluster centre). The algorithm refers to the cluster centre of the labelled sample dataset during the clustering process and uses the information of the labelled samples to guide the unlabelled samples during clustering to improve the clustering performance. We compare the SFCM_V algorithm with the current popular semisupervised clustering algorithm and an unsupervised clustering algorithm and perform experiments on real patient mammogram images using DICE and IoU as evaluation metrics;SFCM_V has the highest evaluation metric coefficient. Experiments demonstrate that SFCM_V has higher segmentation accuracy not only for larger lesion regions, such as tumours, but also for smaller lesion regions, such as calcified spots, compared with existing clustering algorithms.

关键词： Medical image segmentation semisupervised fuzzy clustering algorithm mammogram

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Examination on image segmentation method of ischemic optic neuropathy based on fuzzy clustering theory

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2020年第4期38卷 3625-3633页

作者： Li, Hui Jilin Med Univ Ophthalmol Affiliated Hosp Jilin 132000 Jilin Peoples R China

The purpose is to use medical image processing technology to avoid the influence of subjective factors through the mutual penetration and development of clinical medicine and computer science. Can diagnose the degree of malignancy of ischemic optic neuropathy as quickly as possible, and can take an effective treatment plan for the patient early. Therefore, image segmentation of ischemic optic neuropathy based on fuzzy clustering theory is particularly important for the diagnosis of disease in patients. This paper analyzes the research status of medical image segmentation at home and abroad and the development trend in this aspect in China. Discussed the fuzzy C-means clustering (FCM) image segmentation algorithm in depth, studied the effects of iterative cutoff error, initial clustering center, number of clustering categories and fuzzy weighted index on the practical application of the algorithm. At the same time, the traditional algorithm is not sensitive to the spatial information of the image, making the algorithm sensitive to noise. Firstly, introduced the spatial information of the image, and introduced the algorithm based on spatial information constraint, Based on the above description and based on the neighborhood properties described by the two-dimensional histogram, studied and proposed a relatively easy to understand multidimensional distance measurement method. That is, the two-dimensional pixel value and the neighborhood pixel value viewpoint that can be updated in the two-dimensional direction, by setting a clustering objective function, a clustering measurement method includes neighborhood information. Through the above two-dimensional image segmentation algorithm based on neighborhood spatial information, proposed an image segmentation algorithm for ischemic optic neuropathy of fuzzy kernel clustering theory combined with spatial information. The experimental results show that the proposed algorithm can show excellent results in ischemic neuropathy i

关键词： Medical image segmentation fuzzy c-means kernel method fuzzy clustering algorithm spatial information

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fuzzy clustering procedures for conical fuzzy vector data

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fuzzy SETS AND SYSTEMS 1999年第2期106卷 189-200页

作者： Yang, MS Liu, HH Chung Yuan Christian Univ Dept Math Chungli 32023 Taiwan

This note addresses fuzzy cluster analysis of conical fuzzy vector data. A fuzzy vector represents fuzzy data in a high dimension. In this note a robust type of fuzzy clustering algorithm with a noise cluster is proposed. The proposed algorithm is robust with respect to noise and can also detect outliers for conical fuzzy vector data. Finally, some numerical examples are presented. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： cluster analysis fuzzy clustering algorithm conical fuzzy vector fuzzy c-means clustering noise cluster robustness

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A survey of fuzzy clustering validity evaluation methods

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INFORMATION SCIENCES 2022年 618卷 270-297页

作者： Wang, Hong -Yu Wang, Jie-Sheng Wang, Guan Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

As an unsupervised learning method, clustering does not need to know prior knowledge of the datasets in advance. How determining the optimal number of clusters becomes an important method to judge the quality of clustering results. For fuzzy clustering algo-rithms, the introduction to fuzzy partition makes it more consistent with the structure of real datasets than hard clustering algorithms. Therefore, it is necessary to carry out the research on the validity evaluation methods of fuzzy clustering. At present, the research on fuzzy clustering validity mainly focuses on the fuzzy clustering validity index (FCVI) and the combined fuzzy clustering validity evaluation method (CFCVE). From these two aspects, this paper reviews fuzzy clustering validity functions and combined fuzzy clustering validity evaluation methods. Then FCVI and CFCVE are discussed in details from different points on fuzzy clustering validity functions, and the research status and con-struction strategies of different fuzzy clustering validity evaluation methods are analyzed. The accuracy and stability of each fuzzy clustering validity evaluation method are analyzed through comparative experiments. Finally, the paper summarizes the shortcomings and advantages of the current research on fuzzy clustering validity and looks forward to the research direction and improved methods of the evaluation methods.(c) 2022 Elsevier Inc. All rights reserved.

关键词： fuzzy clustering algorithm fuzzy clustering validity function Combined fuzzy clustering evaluation Optimal clustering number

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A personalized recommendation system for teaching resources in sports using fuzzy C-means clustering technique

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SOFT COMPUTING 2024年第1期28卷 703-720页

作者： Chen, Jiayong Zhou, Guangzhen Zhong, Yize Cent China Normal Univ Sch Phys Educ & Sports Wuhan 430070 Hubei Peoples R China

Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education institutes. Due to the abundance of information, it is challenging to identify useful materials in general, but finding the right materials for students is particularly challenging. To address this issue, this paper aims to study the design of a personalized sports teaching resource recommendation system using a fuzzy clustering technique. To do so, we collected relevant data from entities such as students and teachers, which includes a range of attributes related to physical education, including curricular materials, student profiles, past performance records, and resource metadata. The collected data were then preprocessed to prepare it for further analysis. The features, preferences, and learning styles of each student are examined to develop student profiles based on the data that have been collected. A database schema was created that stored all the information related to physical education teaching resources, students, and teachers. The fuzzy C-means clustering algorithm is used to improve the collaborative filtering recommendation algorithm and reduce the data sparsity of the teaching resources recommendation algorithm. Through a series of experiments, it has been proven that the system designed in this paper can recommend suitable learning resources for different learners and has good performance. At the same time, the recommended method has higher recommendation accuracy and can effectively improve the quality of physical education teaching.

关键词： Data mining Personalized teaching Recommendation of teaching resources fuzzy clustering algorithm

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State-space reconstruction and prediction of chaotic time series based on fuzzy clustering

State-space reconstruction and prediction of chaotic time se...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Abonyi, J Feil, B Nemeth, S Arva, P Babuska, R Univ Veszprem Dept Proc Engn H-8021 Veszprem Hungary

ISBN: (纸本)0780385667

Selecting the embedding dimension of a dynamic system is a key step toward the analysis and prediction of nonlinear and chaotic time-series. This paper proposes a clustering-based algorithm for this purpose. The clustering is applied in the reconstructed space defined by the lagged output variables. The intrinsic dimension of the reconstructed space is then estimated based on the analysis of the eigenvalues of the fuzzy cluster covariance matrices, while the correct embedding dimension is inferred from. the prediction performance of the local models of the clusters. The main advantage of the proposed solution is that three tasks are simultaneously solved during clustering: selection of the embedding dimension, estimation of the intrinsic dimension, and identification of a model that can be used for prediction.

关键词： state space reconstruction MIMO model identification fuzzy clustering algorithm chaotic time series

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Reactive Power Planning Based on fuzzy clustering and Multivariate Linear Regression

Reactive Power Planning Based on Fuzzy Clustering and Multiv...

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IEEE-Power-and-Energy-Society General Meeting

作者： Wang, Yurong Li, Fangxing Wan, Qiulan Univ Tennessee Dept EECS Knoxville TN 37996 USA Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

ISBN: (纸本)9781424483570

This paper proposes a new approach for reactive power planning (RPP) or VAR Planning with two major steps. First, the fuzzy clustering algorithm is employed to select candidate locations for installing new shunt VAR sources. Second, a piecewise linear method is proposed for VAR capacity optimization via minimizing the total cost for system operation. In the cost minimization model, the tie-line total transfer capability (TTC) is modeled with multivariate linear regression algorithm. Test results on the IEEE 30-bus system are presented to clearly demonstrate that the combination of the fuzzy clustering algorithm and the VAR optimization model may be a promising method for RPP.

关键词： fuzzy clustering algorithm Linear Regression Reactive Power Planning (RPP) Optimization Total Transfer Capability (TTC)

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Non-destructive Identification of the geographical origin of red jujube by near-infrared spectroscopy and fuzzy clustering methods

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INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2023年第2期26卷 3275-3290页

作者： Hu, Caiping Xu, Hongjia Fu, Zhaoming Wu, Bin Zhang, Rui Zhi, Chenhong Jinling Inst Technol Sch Comp Engn Nanjing Peoples R China Jiangsu Univ Jingjiang Coll Dept Elect & Informat Engn Zhenjiang Peoples R China ChuZhou Polytech Dept Informat Engn Chuzhou 239000 Peoples R China

The red jujube quality is closely associated with its place of origin. In order to quickly and easily identify the geographical origin of red jujube, the classification of red jujube samples' near-infrared reflectance (NIR) spectra was performed using several fuzzy clustering methods in combination with principal component analysis (PCA) and linear discriminant analysis (LDA). Firstly, a NIR-M-R2 portable near-infrared spectrometer was used to collect four varieties of red jujube samples from four representative producing areas in four provinces: Gansu, Henan, Shanxi and Xinjiang in China. Each variety corresponded to a producing area, and it had 60 samples with a total of 240 samples. Near-infrared spectra of red jujube were acquired using a NIR-M-R2 portable near-infrared spectrometer, and the initial near-infrared spectra were preprocessed by Savitzky-Golay (SG) filtering. Secondly, PCA and LDA were used to further process the NIR data for dimension reduction and feature extraction, respectively. Finally, red jujube samples were classified by fuzzy C-means (FCM) clustering, Gustafson-Kessel (GK) clustering and possibility fuzzy C-means (PFCM) clustering. When GK served as the clustering algorithm, the clustering accuracy was the highest, as the value of 98.8%. Based on the experimental results, it was evident that the GK clustering algorithm played a significant role in identifying the place of origin of red jujube with near-infrared spectroscopy.

关键词： red jujube near-infrared spectroscopy linear discriminant analysis fuzzy C-means fuzzy clustering algorithm Gustafson-kessel possibility fuzzy C-means clustering algorithm

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Segmentation of Anterior and Posterior Chambers on UBM Images by fuzzy clustering

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IEEE ACCESS 2024年 12卷 178805-178815页

作者： Yao, Yaru Wang, Xin You, Xinying Liu, Xueyan Liaocheng Univ Sch Math Sci Liaocheng 252000 Shandong Peoples R China Liaocheng Peoples Hosp Dept Ophthalmol Liaocheng 252000 Shandong Peoples R China

To assess various segmentation methods for the anterior and posterior chambers in ultrasonic biomicroscopy (UBM) images. UBM images were collected from 102 patients diagnosed with primary angle-closure glaucoma, and the corresponding intraocular pressure (IOP) values were measured. The UBM images are manually segmented using ImageJ software as the gold standard, while automatic segmentation employs six distinct methods: Otsu threshold, K-means, fuzzy C-means, robust self-sparse fuzzy clustering algorithm, spatial intuitionistic fuzzy C-means algorithm, and fast robust fuzzy C-means (FRFCM) algorithm. The segmentation results were used to quantify the anterior chamber depth and the area, perimeter, and height of the posterior chamber. Quantitative analyses of the accuracy and reliability of the segmentation quantification results were conducted using relative error and intraclass correlation coefficients (ICC). A total of 408 clear enough UBM images were used for segmentation. The ICC values of the quantitative results of the FRFCM method are 0.996, 0.970, 0.986, and 0.970, outperforming the other five segmentation methods. It excels in accuracy, reliability, precision comparable to manual techniques, and surpasses them in reproducibility and efficiency. Thus, it can be used for semi-automatic analysis of both anterior and posterior chamber regions in UBM images. Additionally, Spearman correlation analysis was performed to explore the relationship between IOPs and the four quantification results. The Spearman correlation coefficients between IOP and anterior chamber depth, posterior chamber area, posterior chamber perimeter, and posterior chamber height were -0.0991, -0.0043, -0.0321, and 0.0757, respectively. There is no significant correlation between IOP and anterior chamber depth, posterior chamber area, perimeter, or height.

关键词： Anterior chamber fuzzy clustering algorithm fuzzy clustering algorithm image segmentation image segmentation posterior chamber posterior chamber ultrasound biomicroscopy ultrasound biomicroscopy

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Vehicle routing problem based on a fuzzy customer clustering approach for logistics network optimization

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2015年第4期29卷 1427-1442页

作者： Wang, Yong Ma, Xiaolei Xu, Maozeng Wang, Yinhai Liu, Yong Chongqing Jiaotong Univ Sch Management Chongqing Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Univ Washington Dept Civil & Environm Engn Seattle WA 98195 USA

Vehicle routing problem (VRP) is a combinatorial optimization and integer programming problem seeking to service a number of customers with a fleet of vehicles. Customer characteristics are neglected in traditional VRPs in the past due to the heterogeneity and ambiguousness. This study presents a vehicle route optimization model in consideration of customer characteristics with three major components: (1) A hierarchical analysis structure is developed to convert customers' characteristics into linguistic variables, and fuzzy integration method is used to map the sub-criteria into higher hierarchical criteria based on the trapezoidal fuzzy numbers;(2) A fuzzy clustering algorithm based on Axiomatic fuzzy Set is proposed to group the customers into multiple clusters;(3) The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach is integrated into the dynamic programming approach to optimize vehicle routes in each cluster. A numerical case study in Anshun, China demonstrates the advantages of the proposed method by comparing with the other two prevailing algorithms. In addition, a sensitivity analysis is conducted to capture the impacts of various evaluation criteria weights. The results indicate our approach performs very well to identify similar customer groups and incorporate individual customer's service priority into VRP.

关键词： Vehicle routing problem axiomatic fuzzy set fuzzy clustering algorithm dynamic programming approach technique for order preference by similarity to ideal solution (TOPSIS)

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