Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Chen, Haipeng; Xie, Zhijie; Huang, Yongping; Gai, Di

doi:10.3390/s21030696

Cited by 15 publications

(6 citation statements)

References 36 publications

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“…Magnetic resonance imaging (MRI) is a commonly used technology in clinical imaging examination, which is widely used in the diagnosis of many diseases and has been unanimously recognized by experts. Fuzzy C-means clustering algorithm (FCM) assisted MRI brain image segmentation has achieved certain results in the field of medical and computer image processing [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] MRI Imaging Omics and Risk Factors Analysis of PWMD in Premature Infants Based on Fuzzy Clustering Algorithm

Wang

Hao

Sun

et al. 2022

Contrast Media & Molecular Imaging

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The magnetic resonance imaging (MRI) characteristics of periventricular white matter damage (PWMD) in premature infants using the fuzzy c-means clustering algorithm (FCM) is explored, and the influencing factors are further clarified. A total of 100 premature infants admitted to the neonatal department of our hospital from February 2020 to February 2022 are selected for in-depth investigation. According to the occurrence of PWMD, they are divided into the PWMD group and the simple premature delivery group, with 50 cases in each group. All preterm infants are examined by MRI and the changes in image characteristics and apparent diffusion coefficient (ADC) values are analyzed. Clinical information of the subjects is collected and the influencing factors of PWMD in preterm infants are analyzed by multivariate regression analysis. In the first magnetic resonance imaging (MRI) examination, the cases of punctured, clustered, and linear lesions are 28 cases, 12 cases, and 10 cases, respectively. The experimental results showed that PWMD of preterm infants presented punctate, clustered, and high linear T1 signal MRI manifestations, which caused a downward trend of ADC value, and caused respiratory distress, low birth weight, premature rupture of membranes, respiratory tract infection, and other risk symptoms.

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Section: Introductionmentioning

confidence: 99%

[Retracted] MRI Imaging Omics and Risk Factors Analysis of PWMD in Premature Infants Based on Fuzzy Clustering Algorithm

Wang

Hao

Sun

et al. 2022

Contrast Media & Molecular Imaging

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“…Fuzzy C-means clustering algorithm is a kind of unsupervised learning algorithm, which introduces fuzzy theory, expresses the probability of a sample belonging to a certain class by membership degree, and classifies data points into similar classes, which has ambiguity and uncertainty. e combination of cluster analysis and fuzzy theory is more in line with the actual data distribution, more suitable for practical applications, has a strong advantage in the analysis and processing of large amounts of data, and can better reflect the actual distribution of data [13,14].…”

Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

Teacher Allocation and Evaluation Based on Fuzzy C-Means Clustering

Li¹,

Chen²,

Pei³

et al. 2022

Mobile Information Systems

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As an important resource in the education industry, teachers play a key role in promoting the high quality of education. In order to explore the overall level of the teaching staff in various regions of China and the allocation level of teachers’ educational resources, this paper selects the data or proportion of the student-teacher ratio, teachers’ educational background, and teachers’ professional titles as indicators. The clustering algorithm based on Fuzzy C-means conducts a cluster analysis on the allocation of compulsory education teachers in 31 provinces in China. The allocation of teachers in 31 provinces is divided into four categories, and the analysis and evaluation are carried out. At the same time, according to the current situation of teacher allocation in compulsory education, relevant countermeasures and suggestions are put forward to improve the overall balanced development of teacher allocation in China and narrow the gap of teacher allocation in China, in order to promote the balanced development of teacher allocation in compulsory education.

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“…Even though the time complexity of segmentation process was reduced, the lack of colour information decreased the target segmentation rate. For obtaining high segmentation accuracy, an intuitionistic fuzzy C‐means algorithm based on membership information transfer‐ring and similarity measurements (IFCM‐MS) [24] was published. Although the integration of spatial information and local image distribution information enormously promoted the segmentation ability of the algorithm, the deficiency of gradient information still resulted in hypersensitivity to chromatic aberration, leading to incomplete object extraction, pixel misclassification, and low accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement

Zeyu,

Xiao,

Defang

et al. 2023

IET Image Processing

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The fuzzy C‐means clustering (FCM) algorithm is widely used in greyscale and colour image segmentation, especially in real colour images. However, in the process of interested regions extraction, it performs barely satisfactory due to the use of single distance metric in traditional FCM. In order to address the issue, a fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement (FCM‐SM) are proposed. First of all, since superpixel image adapts well to irregular image boundaries, the FCM algorithm is elevated to the superpixel level. Moreover, the superpixel merging module based on colour similarity is introduced to improve the traditional watershed transformation by incorporating more gradient and intensity information. Finally, the multi‐feature adaptive fusion measurement is designed in the clustering process to incorporate multiple sources of spatial information, which comprehensively measures the information from different fields to enhance the segmentation capability further. Experiments performed on Berkeley Segmentation Dataset and Brain Tumor Segmentation Dataset demonstrate that the proposed algorithm provides better segmentation results than benchmarks during the object extraction.

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Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Cited by 15 publications

References 36 publications

[Retracted] MRI Imaging Omics and Risk Factors Analysis of PWMD in Premature Infants Based on Fuzzy Clustering Algorithm

[Retracted] MRI Imaging Omics and Risk Factors Analysis of PWMD in Premature Infants Based on Fuzzy Clustering Algorithm

Teacher Allocation and Evaluation Based on Fuzzy C-Means Clustering

Fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement

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