An efficient brain mass detection with adaptive clustered based fuzzy C-mean and thresholding

El‐Khamy, Said E.; Sadek, Rowayda A.; El-Khoreby, Mohamed A.

doi:10.1109/icsipa.2015.7412229

Cited by 14 publications

(4 citation statements)

References 6 publications

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“…The FCM clustering algorithm is the most widely used partition-based clustering algorithm. FCM clustering with an automatically determined number of clusters could enhance the detection accuracy; it uses the Euclidian distance measure 10 . The FCM clustering algorithm gives the best results for overlapped datasets and is comparatively better than k -means and hierarchical clustering algorithms 11 .…”

Section: Methodsmentioning

confidence: 99%

Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020

et al. 2022

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BackgroundThis study is aimed at evaluating the relationship between the number of days elapsed since a country’s first case(s) of coronavirus disease 2019 (COVID-19), the total number of tests conducted, and outbreak indicators such as the total numbers of cases, deaths, and patients who recovered. The study compares COVID-19 indicators among countries and clusters them according to similarities in the indicators. MethodsDescriptive statistics of the indicators were computed and the results were presented in figures and tables. A fuzzy c-means clustering algorithm was used to cluster/group the countries according to the similarities in the total numbers of patients who recovered, deaths, and active cases. ResultsThe highest numbers of COVID-19 cases were found in Gibraltar, Spain, Switzerland, Liechtenstein and Italy were also of that order with about 1500 cases per million population. Spain and Italy had the highest total number of deaths, which were about 140 and 165 per million population, respectively. In Japan, where exposure to the causative virus was longer than in most other countries, the total number of deaths per million population was less than 0.5. According to cluster analysis, the total numbers of deaths, patients who recovered, and active cases were higher in Western countries, especially in central and southern European countries, which had the highest numbers when compared with other countries.ConclusionThere may be various reasons for the differences between the clusters obtained by fuzzy c-means clustering. These include quarantine measures, climatic conditions, economic levels, health policies, and the duration of the fight against the outbreak

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Section: Methodsmentioning

confidence: 99%

Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020

et al. 2022

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“…Fuzzy K-means algorithm This algorithm differs from K-means through use of weighted squared errors [15]. The fuzzy k-means based on [16] 2 ( )…”

Section: Mathematical Approachesmentioning

confidence: 99%

3D volume reconstruction of brain tissues using nonlinear filters, k-means clustering, and Bland-Altman analysis

Pana

Moldoveanu

Moraru

2021

Ann_UGAL_Math_Phys_Mec

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This paper aims to provide a sound estimation of the true value and proportion of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) of the brain DTI images for a proper 3D volume reconstruction. During the pre-processing stage, two nonlinear filters are operated, i.e. bilateral and anisotropic diffusion. The segmentation of each brain tissue is performed using the k-means clustering algorithm. To minimize filters bias and for obtaining the best reproducible results, a statistical analysis has been performed. Thus, the skewness and kurtosis statistics features were computed for each segmented brain tissue and filter. The fuzzy k-means method allows for clustering analysis and the Bland-Altman analysis investigates the agreement between two filtering techniques of the same statistics feature and brain tissue. Then the 3D reconstruction method is presented using ImageJ and the image stacks for raw and processed data. We conclude that anisotropic diffusion filter offers the best results and 3D reconstruction of brain tissues is feasible.

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“…Then, using Lagrange multiplier method to find the and , which make get lowest. When the algorithm converges, the fuzzy clustering will finish [23].…”

Section: Fuzzy Clusteringmentioning

confidence: 99%

Bearing degradation assessment based on entropy with time parameter and fuzzy c-means clustering

Liu¹,

Zhao²,

Li³

et al. 2019

J. vibroeng.

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Bearings are one of the most crucial elements in rotating machine. The condition of bearings decides the operation of machine. Consequently, it is necessary to study the assessment of bearing degradation in order to develop condition-based maintenance. This paper improves an indicator based on entropy which is calculated by wavelet packet decomposition and auto-regressive model. By introducing time parameter, the indicator solves the problem of instability in the initial stage of operation and it is less influenced by the operational conditions. Then, fuzzy c-means clustering can evaluate the process of degradation. Moreover, it can provide the threshold adaptively and help to repair by unit replacement. To ensure the applicability, the data of this paper comes from two laboratories, FEMTO-ST Institute and Intelligent Maintenance System Center. The result indicates that the method is effective to assess bearing degradation process.

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An efficient brain mass detection with adaptive clustered based fuzzy C-mean and thresholding

Cited by 14 publications

References 6 publications

Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020

Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020

3D volume reconstruction of brain tissues using nonlinear filters, k-means clustering, and Bland-Altman analysis

Bearing degradation assessment based on entropy with time parameter and fuzzy c-means clustering

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