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检索条件"主题词=Fuzzy C-Means Algorithm"

共 259 条记录，以下是201-210 订阅

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A Novel Approach for color Image Segmentation Using Iterative Partitioning Mean Shift clustering algorithm

A Novel Approach for Color Image Segmentation Using Iterativ...

引用

2015 International conference on communications and Signal Processing (IccSP)

作者： Naik, P. Pedda Sadhu Gopal, T. Venu JNTUK CSE Dept Kakinada India JNTUHCE CSE Dept Jagityal India

ISBN: (纸本)9781479980819

Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly medical applications. Here in this paper, we here supposed to propose a novel image segmentation using iterative partitioning mean shift clustering algorithm, which overcomes the drawbacks of conventional clustering algorithms and provides a good segmented images. Simulation performance shows that the proposed scheme has performed superior to the existing clustering methods.

关键词： Image segmentation H&E stain images fuzzy c-means algorithm mean shift clustering

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A Lore-Based FcM algorithm for Gallstone cT Images Segmentation 10

A Lore-Based FCM Algorithm for Gallstone CT Images Segmentat...

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

作者： Wang Hong-yan Lv Ji-xing Hebei Finance Univ Dept Basic Sci Baoding Peoples R China Agr Univ Hebei Informat Sci & Technol Coll Baoding Peoples R China

ISBN: (纸本)9781479966004

To acquire more accurate cT image segmentation results of gallstone, this paper presents a Lore-Based FcM algorithm to conquer the deficiency of tradition FcM method. A penalty term is introduced to objective function to enlarge the range of specified class and achieve higher segmentation accuracy. The result of simulation shows that Lore-Based FcM can obviously improve the segmentation quality. The improved algorithm is more rapid and efficiency than the traditional FcM.

关键词： image segmentation medical image computed tomography fuzzy c-means algorithm

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A New Parallel Approach to fuzzy clustering for Medical Image Segmentation

A New Parallel Approach to Fuzzy Clustering for Medical Imag...

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4th International Symposium on Visual computing

作者： Van Luong, Huynh Kim, Jong Myon Univ Ulsan Sch Comp Engn & Informat Technol Ulsan 680749 South Korea

ISBN: (纸本)9783540896388

Medical image segmentation plays an important role in medical image analysis and visualization. The fuzzy c-means (FcM) is one of the well-known methods in the practical applications of medical image segmentation. FcM, however, demands tremendous computational throughput and memory requirements due to a clustering process in which the pixels are classified into the attributed regions based on the global information of gray level distribution and spatial connectivity. In this paper, we present a parallel implementation of FcM using a representative data parallel architecture to overcome computational requirements as well as to create an intelligent system for medical image segmentation. Experimental results indicate that our parallel approach achieves a speedup of 1000x over the existing faster FcM method and provides reliable and efficient processing on cT and MRI image segmentation.

关键词： Medical image segmentation fuzzy c-means algorithm parallel processing data parallel architectures MRI images

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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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comparison of fuzzy and Neural Network Models to Diagnose Breast cancer

Comparison of Fuzzy and Neural Network Models to Diagnose Br...

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1st International conference on Logic, Information, control and computation

作者： Hameed, W. Abdul Bagavandas, M. C Abdul Hakeem Coll Dept Math Melvisharam India SRM Univ Sch Publ Hlth Madras 603203 Tamil Nadu India

ISBN: (纸本)9783642192623

The automatic diagnosis of breast cancer is an important, real-world medical problem. A major class of problems in Medical Science involves the diagnosis of disease, based upon various tests performed upon the patient. When several tests are involved, the ultimate diagnosis may be difficult to obtain, even for a medical expert. This has given rise, over the past few decades, to computerized diagnostic tools, intended to aid the Physician in making sense out of the confusion of data. This Paper carried out to generate and evaluate both fuzzy and neural network models to predict malignancy of breast tumor, using Wisconsin Diagnosis Breast cancer Database (WDBc). Our objectives in this Paper are: (i) to compare the diagnostic performance of fuzzy and neural network models in distinction between malignance and benign patterns, (ii) to reduce the number of benign cases sent for biopsy using the best model as a supportive tool, and (iii) to validate the capability of each model to recognize new cases.

关键词： Breast cancer neural network fuzzy logic learning vector quantization 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...

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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 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

引用

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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