Spatial Rough Intuitionistic Fuzzy C-Means Clustering for MRI Segmentation

Kala, R.; Deepa, P.

doi:10.1007/s11063-021-10441-w

Cited by 9 publications

(3 citation statements)

References 45 publications

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“…In addition, the particularity and uniqueness of national costumes make it need to spend a lot of human and material resources to store in the process of research. At the same time, many ethnic groups live in relatively remote areas and their national costumes are not easy to carry and find [4]. Therefore, the protection, inheritance, effective utilization, and promotion of national costumes have always been the problems to be solved in the development of national culture.…”

Section: Introductionmentioning

confidence: 99%

Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

Hai

2022

Wireless Communications and Mobile Computing

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National costume contains rich patterns and patterns with national characteristics, which is an important part of the study of national costume culture. Neither the traditional protection of national costumes nor the early digital protection can clearly and effectively preserve the details of national costumes, which is not conducive to the development and research of national costume culture. The development of image processing technology provides new technical support for the protection and inheritance of national costumes. This paper proposes the application of spatial neighborhood fuzzy c-means algorithm in 3D image segmentation of national clothing. Based on the traditional fuzzy c-means algorithm, combined with spatial information and gray information, a 3D image segmentation model based on spatial neighborhood fuzzy c-means is constructed. The experimental results show that the 3D image segmentation model based on spatial neighborhood fuzzy c-means has faster convergence speed and better algorithm performance than the improved fuzzy c-means algorithm and has better image segmentation effect in different noise levels. In the 3D image segmentation of national costumes, more detailed information can be retained on the basis of maintaining high accuracy and effect.

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Section: Introductionmentioning

confidence: 99%

Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

Hai

2022

Wireless Communications and Mobile Computing

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“…In recent years, thanks to the big application areas of IFCM, it has developed quite well-liked algorithms, and there are also many interesting applications in areas biology, medicine, engineering, economics and finance [14][15][16][17][18][19][20][21]. It should be noted that the FCM clustering algorithms have been studied since the first time the COVID-19 outbreak appeared in the literature.…”

Section: Introductionmentioning

confidence: 99%

Using Intuitionistic Fuzzy C-Means Clustering Algorithms to Model Covid-19 Cases for Countries in the Worldwide

Ince¹,

Şentürk

2023

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Every day, the number of newly confirmed cases of coronavirus (COVID-19) rises in many countries. It is critical to adjust policies and plans in order to investigate the relationships between the distributions of the spread of this virus in other countries. During this study, the intuitionistic fuzzy c-means (IFCM) clustering method is used to compare and cluster the distributions of COVID-19 spread in 62 countries. Using the IFCM clustering algorithm, the study aims to cluster the countries that use environmental, economic, social, health, and related measurements that affect disease spread to implement policies that regulate disease spread. As a result, countries that have similar factors can take proactive measures to address the pandemic. The data are obtained for 62 countries, and six different feature variables (factors associated with the spread of COVID-19) are determined. The data are obtained for 62 countries, and six variables with different characteristics (linked to the spread of COVID-19) are identified. In this study, the IFCM clustering algorithm is used to determine the dynamic behavior of COVID-19 based on real-world data for multiple countries and Turkey around the world. Data analysis is performed through MATLAB 2018a and R programs. The clustering results revealed that the distribution of dissemination in Brazil, India, and the United States was nearly identical and distinct from that of the 59 other countries.

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“…The conventional fuzzy c means algorithm [4] uses fuzzy Euclidean metric, which has been generalized to intuitionistic fuzzy c means in [5]. Mainly, intuitionistic fuzzy clustering algorithms help in solving the problems of medical image segmentation ( [6], [7]), brain image segmentation [8] and MRI segmentation [9]. In general, the evolution of well separated compact clusters over lower dimensional datasets declares the AIFS based clustering algorithm successful.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy c-Ordered Means Clustering Algorithm

Kaushal

Lohani

2022

IEEE Access

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Atanassov intuitionistic fuzzy set (AIFS) has the capability to deal with various uncertain situations, so its popularity among researchers is quite high. It has been observed that Euclidean distance measure based AIFS clustering algorithms perform well on imprecise datasets. The performance of Euclidean measure based clustering algorithms deteriorates due to the presence of outliers/noise within a dataset. In the paper, an extension of the algorithm given by Leski, Jacek M. [Fuzzy Sets and Systems, 286 (2016): 114-133] is proposed as intuitionistic fuzzy c-ordered means clustering algorithm. This paper analyses the functionality of clustering algorithms over outliers/noises based datasets. In IFCOM, an alternate of Euclidean distance known as Loss function is used. Moreover, IFCOM uses intuitionistic fuzzy OWA to combat the ill effects of the noises and outliers. The proposed algorithm exploits a typicality function based weighing ordering approach. The approach assigins lower weights to the outliers. Hence, the catastrophic behavior is not observed in IFCOM while dealing outliers possessing synthetic and UCI machine learning datasets.

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Spatial Rough Intuitionistic Fuzzy C-Means Clustering for MRI Segmentation

Cited by 9 publications

References 45 publications

Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

Using Intuitionistic Fuzzy C-Means Clustering Algorithms to Model Covid-19 Cases for Countries in the Worldwide

Intuitionistic Fuzzy c-Ordered Means Clustering Algorithm

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