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A fuzzy c-means clustering algorithm Based on Spatial context Model for Image Segmentation

INTERNATIONAL JOURNAL OF fuzzy SYSTEMS 2021年第3期23卷 816-832页

作者： Xu, Jindong Zhao, Tianyu Feng, Guozheng Ni, Mengying Ou, Shifeng Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Yantai Univ Sch Optoelect Informat Sci & Technol Yantai 264005 Peoples R China

An improved fuzzy c-means (FcM) algorithm, which is called Reliability-based Spatial context fuzzy c-means (RSFcM), is proposed for image segmentation in this paper. Aiming to improve the robustness and accuracy of the clustering algorithm, RSFcM integrates neighborhood correlation model with the reliability measurement to describe the spatial relationship of the target. It can make up for the shortcomings of the known FcM algorithm which is sensitive to noise. Furthermore, RSFcM algorithm preserves details of the image by balancing the insensitivity of noise and the reduction of edge blur using a new fuzzy measure indicator. Experimental data consisting of a synthetic image, a brain Magnetic Resonance (MR) image, a remote sensing image, and a traffic sign image are used to test the algorithm's performance. compared with the traditional fuzzy c-means algorithm, RSFcM algorithm can effectively reduce noise interference, and has better robustness. In comparison with state-of-the-art fuzzy c-means algorithm, RSFcM algorithm could improve pixel separability, suppress heterogeneity of intra-class objects effectively, and it is more suitable for image segmentation.

关键词： clustering fuzzy c-means algorithm Image segmentation Spatial context

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An Image Segmentation Method Based on Improved Krill Herd algorithm and fuzzy c - means clustering algorithm 10

An Image Segmentation Method Based on Improved Krill Herd Al...

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10th IEEE International conference on Intelligent Data Acquisition and Advanced computing Systems - Technology and Applications (IDAAcS)

作者： Wang, Ziwei Ye, Zhiwei Liu, Wei Hu, Mingwei Tang, Yuanzhi Zhang, Li Wei, Ming Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781728140698

Image segmentation is a primary work for machine vi(1) sion and fuzzy c -means (FcM) clustering algorithm is one of the commonest methods. However, FcM is sensitive to the initial clustering center and easily falls into the local optimum solution, while the Krill Herd (KH) algorithm has strong global convergence and high stability. As a result, this paper proposes an image segmentation method based on improved Krill Herd algorithm and FcM (IKH-FcM). First of all, the method uses the K-means algorithm to initialize the initial population of the krill herd, the improved KH algorithm is used to calculate the initial clustering center of FcM, and then FcM clustering is applied to implement image segmentation. Experiments show that the approach has strong global convergence and high stability compared to the original FcM, which is a favorable image segmentation approach for practical work

关键词： image segmentation Krill Herd algorithm fuzzy c-means algorithm K-means algorithm

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Possibilistic Intuitionistic fuzzy c-means clustering algorithm for MRI Brain Image Segmentation

Possibilistic Intuitionistic Fuzzy c-Means Clustering Algori...

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8th Panhellenic conference on Artificial Intelligence

作者： Verma, Hanuman Agrawal, R. K. Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFcM) algorithm is the hybridization of fuzzy c-means (FcM) and possibilistic c-means (PcM) algorithms which overcomes the problem of noise in the FcM algorithm and coincident clusters problem in the PcM algorithm. A major challenge posed in the PFcM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionistic fuzzy c-means (PIFcM) algorithm for Atanassov's intuitionistic fuzzy sets (A-IFS) which includes the advantages of the PcM, FcM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFcM algorithm. The experimental results demonstrate that the proposed algorithm yields better result.

关键词： Possibilistic c-means algorithm fuzzy c-means algorithm A-IFS intuitionistic fuzzy c-means algorithm magnetic resonance imaging image segmentation

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A Weighted fuzzy c-means clustering algorithm for Incomplete Big Sensor Data 11th

A Weighted Fuzzy c-Means Clustering Algorithm for Incomplete...

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11th china Wireless Sensor Network conference (cWSN)

作者： Li, Peng chen, Zhikui Hu, Yueming Leng, Yonglin Li, Qiucen Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China South China Agr Univ Coll Nat Resources & Environm Guangzhou 510642 Guangdong Peoples R China

ISBN: (纸本)9789811081231;9789811081224

Sensor data processing plays an important role on the development of the wireless sensor networks in the big data era. Owning to the existence of a large number of incomplete data in wireless sensor networks, fuzzy c-means clustering algorithm (FcM) finds it difficult to produce an appropriate cluster result. The paper proposes a distributed weighted fuzzy c-means algorithm based on incomplete data imputation for big sensor data (DWFcM). DWFcM improves Affinity Propagation (AP) clustering algorithm by designing a new similarity metrics for imputing incomplete sensor data, and then proposes a weighted FcM (wFcM) by assigning a lower weighted value to the incomplete data object for improving the cluster accuracy. Finally, we validate the proposed weighted FcM algorithm on the dataset collected from the smart WSN lab. Experiments demonstrate that the weighted FcM algorithm could fill the missing values very accurately and improve the clustering results effectively.

关键词： Wireless sensor network Big sensor data fuzzy c-means algorithm

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Automatic Brain Tumor Segmentation in Multispectral MRI Volumes Using a fuzzy c-means cascade algorithm 12

Automatic Brain Tumor Segmentation in Multispectral MRI Volu...

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12th International conference on fuzzy Systems and Knowledge Discovery (FSKD)

作者： Szilagyi, Laszlo Lefkovits, Laszlo Benyo, Balazs Budapest Univ Technol & Econ Budapest Hungary Sapientia Univ Transylvania Targu Mures Romania

ISBN: (纸本)9781467376822

The aim of this study was to establish a multi-stage fuzzy c-means (FcM) framework for the automatic and accurate detection of brain tumors from multimodal 3D magnetic resonance image data. The proposed algorithm uses prior information at two points of the execution: (1) the clusters of voxels produced by FcM are classified as possibly tumorous and non-tumorous based on data extracted from train volumes;(2) the choice of FcM parameters (e.g. number of clusters, fuzzy exponent) is supported by train data as well. FcM is applied in two stages: the first stage eliminates the most part of non-tumorous tissues from further processing, while the second stage is intended to accurately extract the tumor tissue clusters. The algorithm was tested on six selected volumes from the BRATS 2012 database. The achieved accuracy is generally characterized by a Dice score in the range of 0.7 to 0.9. Tests have revealed that increasing the size of the train data set slightly improves the overall accuracy.

关键词： Image segmentation tumor detection magnetic resonance imaging fuzzy c-means algorithm semi-supervised clustering

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Possibilistic Intuitionistic fuzzy c-means clustering algorithm for MRI Brain Image Segmentation

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INTERNATIONAL JOURNAL ON ARTIFIcIAL INTELLIGENcE TOOLS 2015年第5期24卷

作者： Verma, Hanuman Agrawal, R. K. Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

关键词： Possibilistic c-means algorithm fuzzy c-means algorithm A-IFS intuitionistic fuzzy c-means algorithm magnetic resonance imaging image segmentation

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An application of sine cosine algorithm-based fuzzy possibilistic c-ordered means algorithm to cluster analysis

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SOFT cOMPUTING 2021年第5期25卷 3469-3484页

作者： Kuo, R. J. Lin, Jun-Yu Thi Phuong Quyen Nguyen Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 106 Taiwan Univ Danang Univ Sci & Technol Fac Project Management 54 Nguyen Luong Bang Danang Vietnam Powertech Technol Inc 12Aly 13Ln 211Linsen E Rd Chiayi 600 Taiwan

Due to advances in information technology, data collection is becoming much easier. clustering is an important technique for exploring data structures used in many fields, such as customer segmentation, image recognition, social science, and so on. However, in real-world applications, there are a lot of noises or outliers which will seriously influence the clustering performance in the dataset. Besides, the clustering results are susceptible to the initial centroids and algorithm parameters. To overcome the influence of outliers on clustering results, this study combines the advantages of probability c-means and fuzzy c-ordered means to propose a fuzzy possibilistic c-ordered means (FPcOM) algorithm. In order to solve the problem of parameters and initial centroids determination, this study employs a sine cosine algorithm (ScA) combined with FPcOM to improve the clustering results. The proposed algorithm is named ScA-FPcOM algorithm. Ten benchmark datasets collected from the UcI machine repository were used to validate the proposed algorithm in terms of adjusted rand index and the Silhouette coefficient. According to the experimental results, the ScA-FPcOM algorithm can obtain better results than other algorithms.

关键词： clustering analysis Sine cosine algorithm Outliers fuzzy c-means algorithm Possibilistic fuzzy c-means algorithm fuzzy c-ordered means algorithm

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fuzzy c-means cluster Analysis Based on Variable Length String Genetic algorithm for the Grouping of Rock Discontinuity Sets

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KScE JOURNAL OF cIVIL ENGINEERING 2020年第11期24卷 3237-3246页

作者： cui, Xuejie Yan, E-chuan China Univ Geosci Fac Engn Wuhan 430074 Peoples R China

Discontinuities have huge impact on civil and mining engineering. To understand the spatial features of discontinuities, it is common to group them into different sets based on orientation. In this paper, a new algorithm is introduced for the identification of discontinuity sets. The new algorithm is developed by combined fuzzy c-means algorithm with variable length string genetic algorithm. In the new method, the number of discontinuity sets is not the necessary input parameter any more. This method is robust, global optimal and totally automatic. To verify its validity, the new method was firstly applied to an artificial data as well as a published data. For artificial data set, the assignment error rate is only 7.4%. For published data set, only 2 discontinuities are assigned to wrong sets. The results indicate that the new algorithm is better than fuzzy c-means algorithm and comparable with other common methods. Afterwards, the new method was utilized to analyze the orientation data sampled at an underground storage cavern site. The new method determines that the ideal number of sets is 3. The new method provided satisfactory results, which confirm its effectiveness and convenience.

关键词： Rock discontinuity fuzzy c-means algorithm Variable length string genetic algorithm Orientation analysis Underground storage cavern

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fuzzy c-means clustering and criminisi algorithm Based Shadow Removal Scheme for Side Scan Sonar Images

Fuzzy C-Means Clustering and Criminisi Algorithm Based Shado...

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IEEE International conference on Innovative Mechanisms for Industry Applications (IcIMIA

作者： Vikas, A. Sreenivasan, A. Sudha, K. L. DSCE Bangalore 78 Karnataka India Dept ECE Bangalore Karnataka India

ISBN: (纸本)9781509059607

This paper proposes the fuzzy c-means and criminisi algorithm Based Shadow removal scheme for the Side Scan Sonar Images. Side Scan Sonar is widely used in the underwater ocean investigations like mining, pipelining, object detection, underwater communications etc. This paper make use of fuzzy c-means clustering algorithm for shadow Region segmentation and criminisi algorithm for filling the shadow region. Thus one can get clear view of detected object.

关键词： fuzzy c-means algorithm criminisi algorithm Side Scan Sonar Ocean investigations

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clustering algorithm Based on fuzzy c-means and Artificial Fish Swarm

Clustering Algorithm Based on Fuzzy C-means and Artificial F...

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International Workshop on Information and Electronics Engineering (IWIEE) / International conference on Information, computing and Telecommunications (IcIcT)

作者： Zhu, Weiling Jiang, Jingqing Song, chuyi Bao, Lanying Inner Mongolia Univ Nationalities Coll Math Tongliao 028000 Peoples R China

clustering algorithm has applied in many fields such as data mining, statistics and machine learning. But the clustering number and the initial clustering center affect the accuracy of clustering. In this paper, the average information entropy and density function are used to determine the clustering number and the initial clustering center respectively based on fuzzy c-means clustering algorithm. And then the new bionic optimization algorithm---artificial fish swarm is applied to cluster. Artificial fish swarm algorithm is simple and easy to implement. The experimental results show the efficiency of the proposed clustering algorithm. (c) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology

关键词： Artificial fish swarm algorithm Information entropy fuzzy c-means algorithm

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