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

INTERNATIONAL JOURNAL ON ARTIFIcIAL INTELLIGENcE TOOLS 2015年第5期24卷 1550016-1550016页

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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Novel local information kernelized fuzzyc-means algorithm for image segmentation

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TEcHNOLOGY 2021年第2期31卷 786-801页

作者： Li, Songcheng Lu, Junyong cheng, Long Li, Xiangping Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Powe Wuhan 430033 Hubei Peoples R China

In MRI, the image with poor quality, especially the image with noise interference or low contrast, may provide insufficient data for the visual interpretation of the affected part. Image segmentation provides an effective method to facilitate early detection and further diagnosis. By introducing a Particle Swarm Optimization (PSO) initialization step and a novel dissimilarity measure metric, we present a local information kernelized fuzzy c-means (LIKFcM) algorithm for image segmentation. The dissimilarity measure metric, considering an adaptive tradeoff weighted factor, incorporates the Mahalanobis distance and outliers-rejection-based spatial term which eliminates unreliable neighboring information. By using this dissimilarity measure metric, the new algorithm could take reliable contextual information into account and achieve better segmentation results on images with complexed boundaries. Furthermore, the adaptive tradeoff factor depends on a fast noise estimation algorithm. This factor avoids subjective adjustment and makes the LIKFcM algorithm more universal. To evaluate the performance of the proposed algorithm both quantitatively and qualitatively, experiments are conducted both on synthetic images and real-world images with different kinds of noise. Segmentation Accuracy (SA) and comparison scores are used to evaluate the performance of both proposed algorithm and other methods. Experimental results illustrate that the proposed algorithm has better performance on denoising and reserving useful edges. The LIKFcM algorithm not only shows more robustness to noise but also preserves the texture details of the images.

关键词： contextual information fuzzy c-means algorithm image segmentation novel dissimilarity metric

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Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and fuzzy c-means Optimization

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JOURNAL OF KING SAUD UNIVERSITY-cOMPUTER AND INFORMATION ScIENcES 2022年第3期34卷 557-566页

作者： Sheela, c. Jaspin Jeba Suganthi, G. Manonmaniam Sunadaranar Univ St Xaviers Autonomous Coll Tirunelveli 627012 Tamil Nadu India Manonmaniam Sunadaranar Univ WC Coll Dept Comp Sci Tirunelveli 627012 Tamil Nadu India

The automatic brain tumor segmentation in MRI (Magnetic Resonance Images) is becoming a challenging task in the field of medicine, since the brain tumor occurs in different shapes, intensities and sizes. This paper proposes an efficient automatic brain tumor segmentation using Greedy Snake Model and fuzzy c-means optimization. This method initially identifies the approximate Region Of Interest (ROI), by removing the non-tumor part by two level morphological reconstruction such as dilation and erosion. A mask is formed by thresholding the reconstructed image and is eroded to improve the accuracy of segmentation in Greedy Snake algorithm. Using the mask boundary as initial contour of the snake, the greedy snake model estimates the new boundaries of tumor. These boundaries are accurate in regions where there is sharp edge and are less accurate where there are ramp edges. The inaccurate boundaries are further optimized by using fuzzy c-means algorithm to obtain the accurate segmentation output. The region that has large perimeter is finally chosen, to eliminate the in-accurate segmented regions. The experimental verification were done on T-1-weighted contrast-enhanced image data set, using the metrics such as dice score, specificity, sensitivity and Hausdorff distance. The proposed method outperforms when compared with the traditional brain tumor segmentation methods in MRI images. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the cc BY-Nc-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： MRI images Image segmentation fuzzy c-means algorithm Greedy snake model

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A Large-Scale Group Decision-Making Method Fusing Three-Way clustering and Regret Theory Under fuzzy Preference Relations

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IEEE TRANSAcTIONS ON fuzzy SYSTEMS 2024年第9期32卷 4846-4860页

作者： Guo, Lun Zhan, Jianming Zhang, chao Xu, Zeshui Hubei Minzu Univ Sch Math & Stat Enshi 445000 Peoples R China Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Shanxi Peoples R China Sichuan Univ Sch Business Chengdu Sichuan Peoples R China

computational intelligence is increasingly applied to complex decision-making challenges, leveraging its data analysis prowess. Hybrid human-artificial intelligence models enhance the grasp of intricate social behaviors, offering valuable insights for social computing and behavior modeling. Within this landscape, large-scale group decision-making (LSGDM) emerges as an invaluable asset for navigating intricate decision-making scenarios. LSGDM enlists the expertise of individuals who articulate their preferences via fuzzy preference relations that abide by additively consistent principles. Its ascendancy is underscored by its applicability and relevance in confronting multifarious decision-making conundrums. In the realm of LSGDM, machine learning methodologies, such as cluster analysis, are deployed to streamline decision-making procedures, particularly when confronted with inherent complexities. The consensus reaching process (cRP) serves as the cornerstone, ensuring that decision makers (DMs) converge on a unified verdict. consequently, comprehensive exploration of cluster analysis and cRP assumes a pivotal role in elevating the effectiveness of LSGDM. To further augment LSGDM, this study leverages a three-way clustering approach grounded in adaptive fuzzy c-mean clustering. This stratagem categorizes DMs into discrete subgroups. Moreover, a consensus metric, embracing both cardinal and ordinal consensus considerations, is established. This metric serves as the foundation for computing DMs' intragroup weights and group weights. Moreover, this article introduces a feedback mechanism imbued with identification and modification rules (directional rules). It incorporates a modification function that takes into account the consensus threshold, DMs' regret psychology, and the consensus level. This modification function methodically derives modification parameters for the spectrum of DMs. Finally, the viability and effectiveness of the LSGDM methodology proffered in this

关键词： consensus reaching process (cRP) fuzzy preference relation (FPR) large-scale group decision-making (LSGDM) three-way clustering (TWc) consensus reaching process (cRP) fuzzy c-means algorithm fuzzy c-means algorithm large-scale group decision-making (LSGDM) three-way clustering (TWc)

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Learning fuzzy decision trees for predicting outcomes of legal cases relating to intellectual property rights

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APPLIED SOFT cOMPUTING 2025年 176卷

作者： Mukhopadhyay, Somnath Mukherjee, Jayati Das, Devangshu chaudhuri, Ashaawari Datta Sarkar, Sunita chaudhuri, Tamal Datta Paul, Kunal Assam Univ Dept Comp Sci & Engn Silchar Assam India Acad Technol Dept Comp Sci & Engn Adisaptagram West Bengal India OP Jindal Global Univ Jindal Sch Banking & Finance Delhi India Bengal Econ Assoc Kolkata West Bengal India

Legal cases involve specific terminology, use past judgments as references, and the entire legal process is expensive, both in terms of time and money. Further, it is not clear at the outset whether the expected judgment will prevail. In the context of trademark and copyright cases, the present paper develops a rule-based system that can be useful, for both lawyers and litigants, as an assisting tool to predict outcomes. The paper proposes a forecasting framework involving TF-IDF weighting scheme, fuzzy c-means algorithm for clustering, the construction of decision trees using Gini Impurity Measure, and using Takagi-Sugeno fuzzy controller for efficient prediction. The dependent variable is binary, and we observe that the combination of specific words and their relative importance has a bearing on the judicial outcome. The paper goes beyond predicting outcomes based on relevant features, and suggests specific rules leading to outcomes of legal proceedings. Accuracy, Balanced Accuracy, Precision, Recall, and F-beta are used as forecasting efficiency metrics and the results indicate moderate forecasting efficiency.

关键词： copyright Trademark Legal arguments TF-IDF weighting scheme fuzzy c-means algorithm Gini Impurity measure Decision tree Takagi-Sugeno fuzzy controller

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An analysis of content-based classification of audio signals using a fuzzy c-means algorithm

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MULTIMEDIA TOOLS AND APPLIcATIONS 2013年第1期63卷 77-92页

作者： Haque, Mohammad A. Kim, Jong-Myon Univ Ulsan Sch Elect Engn Ulsan 680749 South Korea

content-based audio signal classification into broad categories such as speech, music, or speech with noise is the first step before any further processing such as speech recognition, content-based indexing, or surveillance systems. In this paper, we propose an efficient content-based audio classification approach to classify audio signals into broad genres using a fuzzy c-means (FcM) algorithm. We analyze different characteristic features of audio signals in time, frequency, and coefficient domains and select the optimal feature vector by employing a noble analytical scoring method to each feature. We utilize an FcM-based classification scheme and apply it on the extracted normalized optimal feature vector to achieve an efficient classification result. Experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art audio classification systems by more than 11% in classification performance.

关键词： Audio segmentation and classification fuzzy c-means algorithm Multimedia Database retrieval

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An enhanced fuzzy c-means algorithm for audio segmentation and classification

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MULTIMEDIA TOOLS AND APPLIcATIONS 2013年第2期63卷 485-500页

作者： Haque, Mohammad A. Kim, Jong-Myon Univ Ulsan Sch Elect Engn Ulsan 689749 South Korea

Automated audio segmentation and classification play important roles in multimedia content analysis. In this paper, we propose an enhanced approach, called the correlation intensive fuzzy c-means (cIFcM) algorithm, to audio segmentation and classification that is based on audio content analysis. While conventional methods work by considering the attributes of only the current frame or segment, the proposed cIFcM algorithm efficiently incorporates the influence of neighboring frames or segments in the audio stream. With this method, audio-cuts can be detected efficiently even when the signal contains audio effects such as fade-in, fade-out, and cross-fade. A number of audio features are analyzed in this paper to explore the differences between various types of audio data. The proposed cIFcM algorithm works by detecting the boundaries between different kinds of sounds and classifying them into clusters such as silence, speech, music, speech with music, and speech with noise. Our experimental results indicate that the proposed method outperforms the state-of-the-art FcM approach in terms of audio segmentation and classification.

关键词： Audio segmentation and classification fuzzy c-means algorithm Multimedia Database retrieval

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Simulation-Enhanced MQAM Modulation Identification in communication Systems: A Subtractive clustering-Based PSO-FcM algorithm Study

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INFORMATION 2024年第1期15卷 42-42页

作者： Quan, Zhi Zhang, Hailong Luo, Jiyu Sun, Haijun Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FcM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR) environments. We propose an enhanced FcM algorithm that incorporates particle swarm optimization (PSO) to improve the accuracy of recognizing M-ary quadrature amplitude modulation (MQAM) signal orders. The process is a two-step clustering process. First, the constellation diagram of the received signal is used by a subtractive clustering algorithm based on SNR to figure out the initial number of clustering centers. The PSO-FcM algorithm then refines these centers to improve precision. Accurate signal classification and identification are achieved by evaluating the relative sizes of the radii around the cluster centers within the MQAM constellation diagram and determining the modulation order. The results indicate that the Sc-based PSO-FcM algorithm outperforms the conventional FcM in clustering effectiveness, notably enhancing modulation recognition rates in low-SNR conditions, when evaluated against a variety of QAM signals ranging from 4QAM to 64QAM.

关键词： modulation recognition fuzzy c-means algorithm constellation diagram particle swarm optimization algorithm MQAM signal

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Research on storage and encryption method for private electronic communication information based on fuzzy rules

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INTERNATIONAL JOURNAL OF cOMPUTER APPLIcATIONS IN TEcHNOLOGY 2024年第4期74卷 342-350页

作者： Sun, Rui Wang, Zhenzhen Liu, Huanhuan Ji, Yu Hebei Inst Mech & Elect Technol Dept Informat Engn Shijiazhuang Hebei Peoples R China Hebei Inst Mech & Elect Technol Org Dept Shijiazhuang Hebei Peoples R China

To improve the security of private electronic communication information, the paper proposes an information storage encryption method based on fuzzy rules. Firstly, after establishing a private electronic communication information set, fill in the missing values. Secondly, using the fuzzy c-means algorithm, electronic communication information is classified according to fuzzy rules and stored in the underlying information database according to the index of communication information. Finally, for the data in the information database, the AES algorithm is applied to encrypt lightweight information, and a hybrid algorithm of DES and Ecc is applied to encrypt high-dimensional sparse information. The experimental results show that when the number of plaintext bits in private electronic communication information increases from 30 to 150 bits, the number of ciphertext bits in this method increases from 65 to 309 bits, indicating that this method can effectively ensure the security of private electronic communication information.

关键词： private electronic communication information information classification fuzzy c-means algorithm fuzzy rules information encryption

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Image change Detection Based on fuzzy clustering and Neural Networks

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INTERNATIONAL JOURNAL OF ADVANcED cOMPUTER ScIENcE AND APPLIcATIONS 2024年第6期15卷 495-503页

作者： Wang, chenwei Li, Xiating Jiangxi V&T Coll Commun Sch Informat Engn Nanchang 330013 Peoples R China

In the change detection of synthetic aperture radar images, the image quality and change detection accuracy are difficult to meet the application requirements due to the influence of speckle noise. Therefore, the study improved the fuzzy c-means algorithm by introducing fuzzy membership degree and Gabor texture features. Features were weighted through channel attention, resulting in an image change detection model, namely, the fuzzy local information c-means for Gabor textures and multi-scale channel attention wavelet convolutional neural network. The segmentation accuracy of the model was 0.995, which improved by 0.119 compared to the traditional fuzzy c-means algorithm. When adding multiplicative noise with different variances, the noise variance reached 0.30, and the accuracy of the algorithm still reached 0.982. In practical application analysis, the detection and segmentation accuracy of river images was 0.983 with a partition coefficient of 0.935, and the segmentation accuracy of farmland images was 0.960 with a partition coefficient of 0.902. Therefore, the algorithm has good stability and anti-noise performance. The algorithm can be widely applied in various fields of synthetic aperture radar image change detection, such as disaster assessment, urban development monitoring, and environmental change monitoring. This paper provides more accurate analysis results, which help with policy formulation and effective resource management.

关键词： fuzzy c-means algorithm fuzzy membership degree Gabor texture channel attention neural networks synthetic aperture radar images

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