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检索条件"主题词=fuzzy C-means algorithm"

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Hybrid Possibilistic-Genetic Technique for Assessment of Brain Tissues Volume case Study for Alzheimer Patients Images clustering 18

Hybrid Possibilistic-Genetic Technique for Assessment of Bra...

引用

18th IEEE/AcIS International conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing (SNPD)

作者： Lazli, Lilia Boukadoum, Mounir Ait-Mohamed, Otmane Univ Quebec UQAM CoFaMic Res Ctr Dept Comp Sci Quebec City PQ Canada Univ Quebec ETS Dept Elect Engn Quebec City PQ Canada Concordia Univ Dept Elect Engn & Comp Sci Quebec City PQ Canada

ISBN: (纸本)9781509055043

The effect of partial volume related to anatomical MRI and functional images limit the diagnostic potential of brain imaging. To remedy for this problem, we propose a fuzzy-genetic brain segmentation scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes, from brain images of Alzheimer patients from a real database. This clustering process based on Possibilistic c-means (PcM) algorithm, which allows modeling the degree of relationship between each voxels and a given tissue;and based on fuzzy genetic initialization for the centers of clusters by a fuzzy c-means (FcM) algorithm, and for which the result is optimized by genetic process. The visual results show a concordance between the ground truth segmentation and the hybrid algorithm results, which allows efficient tissue classification. The superiority was also proved with the quantitative results of the proposed method in comparison with the both conventional FcM and PcM algorithms.

关键词： Possibilistic clustering Genetic algorithms fuzzy c-means algorithm cluster validity measures Alzheimer patients.

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Using fuzzy clustering to determine agent reputation

Using fuzzy clustering to determine agent reputation

引用

International conference on Data Mining (DMIN 05)

作者： Kaur, H Bedi, P Univ Delhi Dept Comp Sci Delhi 110007 India

ISBN: (纸本)1932415793

Trust is an agent's expectation of other agent's capability, which needs to be confirmed by experience of various peer agents in application domain about capability of agent. Majority view of peer agents materializes into reputation of agent. In our model trustier agent computes reputation based on its own experience as well as experience peer agents have with trustee agent. The trustier agent interacts with peer agents to get their experience information inform of recommendations. The concept of reputation is subjective and Intuitionistic fuzzy Sets (IFS) are used in this paper to model reputation. fuzzy hierarchical agglomerative clustering is done to filter off the noise in IFS recommendations in form of outliers by cutting dendogram at required similarity level. The cluster with maximum number of elements denotes views of majority of recommenders and its center represents reputation Of trustee agent, which is computed using fuzzy c-means algorithm.

关键词： agent Intuitionistic fuzzy Sets fuzzy hierarchical clustering dendogram fuzzy c-means algorithm

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A feature weighted FcM clustering algorithm based on evolutionary strategy

A feature weighted FCM clustering algorithm based on evoluti...

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4th World congress on Intelligent control and Automation

作者： Li, J Gao, XB Ji, HB Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)0780372689

fuzzy c-means (FcM) algorithm is one of effective methods for fuzzy cluster analysis, which has widely used in unsupervised pattern classification. To consider the different contribution of each dimensional feature of the given samples to be classified, this paper presents a novel FcM clustering algorithm based on feature weighted. With clustering validity function as criterion, the proposed algorithm optimizes the weight matrix using evolutionary strategy and obtains better result than the traditional one, which enriches the theory of FcM-type algorithms. The test experiment with real data of IRIS demonstrates the effectiveness of the novel algorithm.

关键词： cluster analysis fuzzy c-means algorithm evolutionary strategy

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A Tower Layered algorithm for Medical Image Segmentation 10

A Tower Layered Algorithm for Medical Image Segmentation

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10th International conference on computer Science and Education (IccSE)

作者： Tao Jia Huang Run-hua Agr Univ HeBei Informat Sci & Technol Coll Baoding Peoples R China Agr Univ HeBei Informat Sci & Technol Coll Baoding Peoples R China

ISBN: (纸本)9781479966004

This paper proposes an algorithm named Tower Layered FcM to improve the traditional FcM method. A Tower layered structure with a constraint is added into the objective function and pixel's neighbor information is rational used. The Tower layered structure can speed up the algorithm and constraint the subordinate degree of pixels. So the subordinate degree of clustering center is made more reasonable. The experimental result shows that the new algorithm can save detail image information and it is more efficient than the traditional FcM.

关键词： image segmentation fuzzy c-means algorithm spatial information tower structure

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Applications of Partition based clustering algorithms: A Survey

Applications of Partition based Clustering Algorithms: A Sur...

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IEEE International conference on computational Intelligence and computing Research (IccIc)

作者： Dharmarajan, A. Velmurugan, T. Bharathiar Univ Ctr Res & Dev Coimbatore 046 Tamil Nadu India DG Vaishnav Coll Res Dept Comp Sci Madras 106 Tamil Nadu India

ISBN: (纸本)9781479915941;9781479915958

Data mining is one of the interesting research areas in database technology. In data mining, a cluster is a set of data objects that are similar to one another with in a cluster and are different to the entities in the former clusters. clustering is the efficient method in data mining in order to process huge data sets. The core methodology of clustering is used in many domains like academic result analysis of institutions. Also, the methods are very well suited in machine learning, clustering in medical dataset, pattern recognition, image mining, information retrieval and bioinformatics. The clustering algorithms are categorized based upon different research phenomenon. Varieties of algorithms have recently occurred and were effectively applied to real-life data mining problems. This survey mainly focuses on partition based clustering algorithms namely k-means, k-Medoids and fuzzy c-means In particular, they applied mostly in medical data sets. The importance of the survey is to explore the various applications in different domains.

关键词： clustering algorithm fuzzy c-means algorithm k-means algorithm k-Medoids algorithm

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Medial Axis Detection from Dental Micro-cT Records

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11th International congress of the IUPESM/World congress on Medical Physics and Biomedical Engineering

作者： Benyo, Balazs Szilagyi, Laszlo Dobo-Nagy, csaba Budapest Univ Technol & Econ Dept Control Engn & Informat Technol Budapest Hungary Semmelweis Univ Fac Dent Dept Prosthet Dent H-1085 Budapest Hungary

ISBN: (纸本)9783642038815

This paper presents a novel image processing procedure dedicated to the automated detection of the medial axis of the root canal from dental micro cT records. The 3D model of root canal is built up from several hundreds of parallel cross sections, using image enhancement and segmentation, center point detection in the segmented slice, three dimensional inner surface reconstruction and morphological skeleton extraction in three dimensions. The central line of the root canal is is interpolated as a 3D spline curve. The proposed procedure can help prepare several kinds of endodontic interventions.

关键词： medial axis detection morphological skeleton fuzzy c-means algorithm

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Multi-stage FcM-based intensity inhomogeneity correction for MR brain image segmentation

Multi-stage FCM-based intensity inhomogeneity correction for...

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18th International conference on Artificial Neural Networks (IcANN 2008)

作者： Szilagyi, Laszlo Szilagyi, Sandor M. David, Laszlo Benyo, Zoltan Budapest Univ Technol & Econ Dept Control Engn & Informat Technol H-1117 Budapest Hungary Sapientia Hungarian Sci Univ Transylvania Fac Tech & Human Sci Targu Mures Romania

ISBN: (纸本)9783540875581

Intensity inhomogeneity or intensity non-uniformity (INU) is ail undesired phenomenon that represents the main obstacle for MR, image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms, and they generally have difficulties when INU reaches high amplitudes. This paper proposes a multiple stage fuzzy c-means (FcM) based algorithm for the estimation and compensation of IN U, by modeling it as a slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothing filter that performs a context dependent averaging, based oil a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The resulting segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.

关键词： intensity inhomogeneity fuzzy c-means algorithm image segmentation context dependent filter magnetic resonance imaging morphological operations

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An Accurate algorithm for Identifying Mutually Exclusive Patterns on Multiple Sets of Genomic Mutations 10th

An Accurate Algorithm for Identifying Mutually Exclusive Pat...

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10th International Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： He, Siyu Wang, Jiayin Zhao, Zhongmeng Zhang, Xuanping Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

ISBN: (纸本)9783031349591;9783031349607

In cancer genomics, the mutually exclusive patterns of somatic mutations are important biomarkers that are suggested to be valuable in cancer diagnosis and treatment. However, detecting these patterns of mutation data is an NP-hard problem, which pose a great challenge for computational approaches. Existing approaches either limit themselves to pair-wise mutually exclusive patterns or largely rely on prior knowledge and complicated computational processes. Furthermore, the existing algorithms are often designed for genotype datasets, which may lose the information about tumor clonality, which is emphasized in tumor progression. In this paper, an algorithm for multiple sets with mutually exclusive patterns based on a fuzzy strategy to deal with real-type datasets is proposed. Different from the existing approaches, the algorithm focuses on both similarity within subsets and mutual exclusion among subsets, taking the mutual exclusion degree as the optimization objective rather than a constraint condition. fuzzy clustering of the is done mutations by method of membership degree, and a fuzzy strategy is used to iterate the clustering centers and membership degrees. Finally, the target subsets are obtained, which have the characteristics of high similarity within subsets and the largest number of mutations, and high mutual exclusion among subsets and the largest number of subsets. This paper conducted a series of experiments to verify the performance of the algorithm, including simulation datasets and truthful datasets from TcGA. According to the results, the algorithm shows good performance under different simulation configurations, and some of the mutually exclusive patterns detected from TcGA datasets were supported by published literatures. This paper compared the performance to MEGSA, which is the best and most widely used method at present. The purities and computational efficiencies on simulation datasets outperformed MEGSA.

关键词： Gene mutation Mutual exclusion clustering Membership degree fuzzy c-means algorithm

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A Novel fuzzy clustering Method with No Outliers Influence

A Novel Fuzzy Clustering Method with No Outliers Influence

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2nd International conference on Mechatronics and Applied Mechanics (IcMAM2012)2nd International conference on Mechatronics and Applied Mechanics (IcMAM2012)

作者： Kao, Li-Jen Huang, Yo-Ping Hwa Hsia Inst Technol Dept Comp Sci & Informat Engn New Taipei City 23568 Taiwan Natl Taipei Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9783037856512

fuzzy c-means (FcM) clustering algorithm can be used to classify hand gesture images in human-robot interaction application. However, FcM algorithm does not work well on those images in which noises exist. The noises or outliers make all the cluster centers towards to the center of all points. In this paper, a new FcM algorithm is proposed to detect the outliers and then make the outliers have no influence on centers calculation. The experiment shows that the new FcM algorithm can get more accurate centers than the traditional FcM algorithm.

关键词： fuzzy c-means algorithm noise detection outlier detection

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Entropy-based Framework Dealing with Error in Software Development Effort Estimation 12

Entropy-based Framework Dealing with Error in Software Devel...

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12th International conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： El Koutbi, Salma Idri, Ali Univ Mohammed 5 ENSIAS Software Projects Management Res Team Rabat Morocco

ISBN: (纸本)9789897582509

Software engineering community often investigates the error concerning software development effort estimation as a part, and sometimes, as an improvement of an effort estimation technique. The aim of this paper is to propose an approach dealing with both model and attributes measurement error sources whatever the effort estimation technique used. To do that, we explore the concepts of entropy and fuzzy clustering to propose a new framework to cope with both error sources. The proposed framework has been evaluated with the cOcOMO'81 dataset and the fuzzy Analogy effort estimation technique. The results are promising since the actual confidence interval percentages are closer to those proposed by the framework.

关键词： Error Software Effort Estimation Entropy fuzzy c-means algorithm fuzzy Analogy

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