Modified possibilistic fuzzy C-means algorithms for segmentation of magnetic resonance image

Aparajeeta, Jeetashree; Nanda, Pradipta Kumar; Das, Niva

doi:10.1016/j.asoc.2015.12.003

Cited by 51 publications

(20 citation statements)

References 21 publications

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“…The fuzzy K‐means (FKM) algorithm is mostly employed for medical image segmentation . Aparajeeta et al suggested a possibilistic based fuzzy approach that supports the eradication of noise levels in the segmentation results. The performance of the process using the spatial information of the nearest pixels is to enhance the brain image segmentation.…”

Section: Related Workmentioning

confidence: 99%

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Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Vigneshwaran

Govindaraj

Murugan

et al. 2019

Int J Imaging Syst Tech

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Human‐made/developed algorithms provide automatic identification and segmentation of the tissues, lesions and tumor regions available in brain magnetic resonance scan images, which invocates predicaments such as high computational cost and low accuracy rate. Such hassles are reconciled with the utilization of an unsupervised approach in combination with clustering techniques. Initially, static features are chosen from the input image, which is fed to the self‐organizing map (SOM), where the algorithm employs the dimensionality reduction of input images. Consecutively, the reduced SOM prototype of data is clustered by the modified fuzzy K‐means (MFKM) algorithm. The MFKM algorithm can be modified in terms of membership variables because it operates with spatial information and converges quickly, and this would be of greater benefit to radiologists as they reduce the wrong predictions and voluminous time that normally occur owing to human involvement. The proposed algorithm provides 98.77% sensitivity and 97.5% specificity, which are better than any other traditional algorithms mentioned in this article.

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Section: Related Workmentioning

confidence: 99%

“…The Dice Overlap Index (DOI) refers to the overlap function and is used to measure the segmentation accuracy with respect to the Ground Truth and the segmented image. 17 The DOI value is expressed using Equation (20).…”

Section: Dice Overlap Indexmentioning

confidence: 99%

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Vigneshwaran

Govindaraj

Murugan

et al. 2019

Int J Imaging Syst Tech

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“…One of the most well-known types of soft clustering is the fuzzy C-means (FCM) approach, and it has demonstrated its superior flexibility versus hard clustering in numerous application domains, such as human motion analysis [36], carbonate fluid identification [38], magnetic resonance image segmentation [3], green supply chain investment [4] and Parkinson's disease diagnosis [21]. The FCM method, grounded on the L2-norm distance, is more suitable to handle the spherical clusters, but it does not play a satisfactory job in handling general clusters [55].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Kernelized Fuzzy Clustering and Multiple Attributes Decision Analysis for Corporate Risk Management

Lin

2016

Int. J. Fuzzy Syst.

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This study introduces an emerging risk management architecture by extending balanced scorecards (BSC) with risk exposure considerations for corporate operating performance assessment and then constructs a hybrid mechanism that combines kernelized fuzzy C-means (KFCM), multiple attributes decision analysis (MADA), and extreme learning machine (ELM) for corporate operating performance forecasting. KFCM is implemented to do the clustering task for each corporate under each aspect of BSC. No specific corporate reaches optimal performance under each assessing measure-that is, dissimilar assessing criteria leads to dissimilar outcomes. This method can be transformed into a MADA task and a MADA algorithm that can yield a reliable outcome systematically. Sequentially, the outcome is fed into ELM to construct the performance forecasting mechanism. The introduced mechanism with outstanding forecasting performance comes with a critical challenge: it lacks interpretability, which impedes its real-life usage. To cope with this problem, the rough set theory (RST) is employed to extract the inherent decision logics from the black-box model and visualize it in human readable formats. The introduced model has been examined by real cases and is a promising alternative for corporate risk management.

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“…The above multi-view clustering approaches are all based on hard clustering in which each object can only belong to one cluster. Since the real world data sets may not be well separated, different approaches have been proposed based on soft or fuzzy clustering algorithms (Aparajeeta et al, 2016;Kannan et al, 2015;Anderson et al, 2013) in which each object can belong to all the clusters with various degrees of memberships. The memberships used in soft clustering help to describe the data better and have many potential applications in the real world.…”

Section: Introductionmentioning

confidence: 99%

Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources

Wang

Chen

2017

Expert Systems with Applications

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Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it. Many approaches have been proposed to improve clustering performance by exploring and integrating heterogeneous information underlying different views. In this paper, we propose a new multi-view fuzzy clustering approach called Mini-maxFCM by using minimax optimization based on well-known Fuzzy c means.In MinimaxFCM the consensus clustering results are generated based on minimax optimization in which the maximum disagreements of different weighted views are minimized. Moreover, the weight of each view can be learned automatically in the clustering process. In addition, there is only one parameter to be set besides the fuzzifier. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed MinimaxFCM are provided here. Experimental studies on nine multi-view data sets including real

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Modified possibilistic fuzzy C-means algorithms for segmentation of magnetic resonance image

Cited by 51 publications

References 21 publications

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Hybrid Kernelized Fuzzy Clustering and Multiple Attributes Decision Analysis for Corporate Risk Management

Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources

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