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Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

Yan Xu, Ruizhi Chen, Yu Li, Peng Zhang, Jie Yang, Xuemei Zhao, Mengyun Liu, Dewen Wu
2019 Remote Sensing  
To overcome these constraints, this study proposes a multispectral image segmentation algorithm based on fuzzy clustering combined with the Tsallis entropy and Gaussian mixture model.  ...  The algorithm uses the fuzzy Tsallis entropy as regularization item for fuzzy C-means (FCM) and improves dissimilarity measure using the negative logarithm of the Gaussian Mixture Model (GMM).  ...  Acknowledgments: The authors would like to thank Tsallis C. for his theoretical support.  ... 
doi:10.3390/rs11232772 fatcat:lejrcqcylnb7bnmqp5a52yesky

Color segmentation by fuzzy co-clustering of chrominance color features

Madasu Hanmandlu, Om Prakash Verma, Seba Susan, V.K. Madasu
2013 Neurocomputing  
This paper proposes a new probabilistic non-extensive entropy feature for texture characterization, based on a Gaussian information measure.  ...  The performance of the new entropy function is found superior to other forms of entropy such as Shannon, Renyi, Tsallis and Pal and Pal entropies on comparison.  ...  One of the axioms of classical probability theory states that in an experiment, as the probability of some event approaches 1 and the probabilities of all other events approach zero, the entropy or uncertainty  ... 
doi:10.1016/j.neucom.2012.09.043 fatcat:vvlnw3uwdnbbvortokpeiogoi4

Identification of Blood Vessel Clot Region using Fuzzy C Means Clustering Based Artificial Bee Colony Algorithm

2019 International Journal of Engineering and Advanced Technology  
In this paper, we put forth a technique that has Fuzzy C-Means clustering and Artificial Bee Colony (ABC) Optimization has delivered the segmentation of MRA brain image.  ...  The unsupervised clustering FCM has produced candidate outcomes in medical image processing.  ...  For the fuzzy clustering, location of points near to the center of a cluster, may be in the cluster to a higher degree than points in the edge of a cluster.  ... 
doi:10.35940/ijeat.a1135.1291s419 fatcat:6wchsqltevhjdbflgcy6oodsvi

Non-extensive entropy algorithm for multi-region segmentation: generalization and comparison - DOI 10.5752/P.2316-9451.2013v1n2p3

Paulo Sergio Rodrigues, Gilson Antonio Giraldi
2013 Abakós  
Recently, studies in mechanical statistics have proposed a new kind of entropy, called Tsallis entropy (or q-entropy or non-extensive entropy), which has been considered with promising results on several  ...  In order to show the robustness of the NESRA performance, we compare it with well known and traditional approaches such as bootstrap, fuzzy c-means, k-means, self-organizing map and watershed image clustering  ...  Bootstrap was ran in order to achieve up five clusters as well as fuzzy c-means and k-means.  ... 
doi:10.5752/p.2316-9451.2013v1n2p3 fatcat:vwnot3e7fna7tjctx4twg5vo6m

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

Eslam Mohammed Abdelkader, Osama Moselhi, Mohamed Marzouk, Tarek Zayed
2020 Intelligent Automation and Soft Computing  
In another study, Suresh and Lal (2017) presented an approach for multi-level thresholding of satellite images based on Tsallis entropy and minimum cross entropy as objective functions.  ...  They concluded that Kapur entropy-based approach provided faster convergence and lower processing time. However, Tsallis entropy-based approach provided better segmentation quality.  ... 
doi:10.32604/iasc.2020.010100 fatcat:esmpqozgfrbr5kykqakqarlsbe

Fuzzy Entropy Based Optimal Thresholding Technique for Image Enhancement

Sesadri U, Siva Sankar B, Nagaraju C
2015 International Journal of Soft Computing  
The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.  ...  The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a rule for management of uncertainty.  ...  However it fails for multi-resolution real images.  ... 
doi:10.5121/ijsc.2015.6202 fatcat:xibloirzp5hxnjprkaauuuqsoy

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

Souad Larabi-Marie-Sainte, Reham Alskireen, Sawsan Alhalawani
2021 Electronics  
It consists of a set of operations to handle an image. Image segmentation is among its main important operations.  ...  The article presents new research directions in image segmentation based on bio-inspired algorithms.  ...  In [37] , the authors presented a hybrid-segmentation method based on Tsallis and Renyi entropies in addition to the GA. The fitness function was used to maximize the entropy.  ... 
doi:10.3390/electronics10243116 fatcat:iusw5vsfxnbbbb47erqpmqaavy

Picture Fuzzy MCDM Approach for Risk Assessment of Railway Infrastructure

Vladimir Simić, Radovan Soušek, Stefan Jovčić
2020 Mathematics  
Secondly, a picture fuzzy hybrid method based on the direct rating, and Tsallis–Havrda–Charvát entropy is provided to prioritize risk factors.  ...  The paper aims to introduce a picture fuzzy group multi-criteria decision-making approach for risk assessment of railway infrastructure.  ...  Tsallis-Havrda-Charvát entropy [69, 70] is a generalized form of Shannon entropy [71] .  ... 
doi:10.3390/math8122259 fatcat:r7hafy656zdelpqfzzje2b4xre

Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-level Thresholding Image Segmentation

Ahmed A. Ewees, Mohamed Abd Elaziz, Mohammed A. A. Al-qaness, Hassan A. Khalil, Sunghwan Kim
2020 IEEE Access  
Besides, the proposed method is evaluated using the fuzzy entropy.  ...  This paper presents a hybrid meta-heuristic approach for multi-level thresholding image segmentation by integrating both the artificial bee colony (ABC) algorithm and the sine-cosine algorithm (SCA).  ...  CONCLUSION The multi-level thresholding approach is used to segment the image (cluster its elements) and is considered as a preprocessing step in many applications.  ... 
doi:10.1109/access.2020.2971249 fatcat:psy7dvisdrebxmzwjwo536adqe

Non-Extensive Entropy for CAD Systems of Breast Cancer Images

Paulo Rodrigues, Ruey-feng Chang, Jasjit Suri
2006 Computer Graphics and Image Processing (SIBGRAPI), Proceedings of the Brazilian Symposium on  
Then, a new kind of entropy, called nonextensive entropy, has been proposed in the literature for generalizing the Shannon entropy.  ...  In this paper, we propose the use of non-extensive entropy, also called q-entropy, applied in a CAD system for breast cancer classification in ultrasound of mammographic exams.  ...  Then, recent developments, based on the concept of nonextensive entropy, also called Tsallis entropy, have generated a new interest in the study of Shannon entropy for Information Theory [10, 17] .  ... 
doi:10.1109/sibgrapi.2006.31 dblp:conf/sibgrapi/RodriguesCS06 fatcat:yxodzj5cgjgz7ky2aak6daff2i

Entropy based Automatic Unsupervised Brain Intracranial Hemorrhage Segmentation using CT images

Indrajeet Kumar, Chandradeep Bhatt, Kamred Udham Singh
2020 Journal of King Saud University: Computer and Information Sciences  
The proposed work is consisting of fuzzy c-mean (FCM), automatic selection of cluster, skull removal, thresholding and edge-based active contour methods.  ...  The seed point for level set method is initialized by entropy based thresholding techniques.  ...  Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper  ... 
doi:10.1016/j.jksuci.2020.01.003 fatcat:ijwhycywdnhq7kb7z2hbxvxce4

Complexity theory, time series analysis and Tsallis q-entropy principle part one: theoretical aspects

George P. Pavlos
2017 Journal of the Mechanical Behavior of Materials  
Finally, we provide a comprehensive description of the novel concepts included in the complexity theory from microscopic to macroscopic level.  ...  in all levels of the physical reality.  ...  This generalization of entropy principle by Tsallis can produce the multilevel, multi scale or long range correlations observed at the complex systems.  ... 
doi:10.1515/jmbm-2017-0023 fatcat:p2bevuxzufg7xfmjjgihgrnczy

State-of-the Art Optimal Multilevel Thresholding Methods for Brain MR Image Analysis

Akankshya Das, Sanjay Agrawal, Leena Samantaray, Rutuparna Panda, Ajith Abraham
2020 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
The brain MR image analysis is a primary non-invasive component to detect any abnormality in the brain. It is a very important application in the field of medical image processing.  ...  Implementation of these segmentation techniques in biomedical engineering is a major breakthrough.  ...  Measurement space clustering (iterative thresholding, clustering thresholding, minimum error, fuzzy clustering), 3. Histogram entropy information (entropic, cross entropic, fuzzy entropic), 4.  ... 
doi:10.18280/ria.340302 fatcat:2kiajolrzrfqfa4nwc5uwd7mcu

Segmentation of images by color features: A survey

Farid Garcia-Lamont, Jair Cervantes, Asdrúbal López, Lisbeth Rodriguez
2018 Neurocomputing  
, region, feature clustering and neural networks.  ...  Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors.  ...  In [170] the multi-level low-rank approximation-based spectral clustering method is proposed to segment high resolution images.  ... 
doi:10.1016/j.neucom.2018.01.091 fatcat:yla45f2hmfasxmrethzmswqoti

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

Omar Sedqi Kareem, Ahmed Khorsheed AL-Sulaifanie, Dathar Abas Hasan, Dindar Mikaeel Ahmed
2021 Asian Journal of Research in Computer Science  
This paper presents a systematic literature review of brain tumor segmentation strategies and the classification of abnormalities and normality in MRI images based on various deep learning techniques,  ...  A brain tumor is a problem that threatens life and impedes the normal working of the human body.  ...  Entropy (WPTE) Bayesian fuzzy clustering DAE based JOA (Jaya Optimization Algorithm) Accuracy = 98.5 % 7 [19] The dataset comprises 3064 T1-weighted NA Multi-level based Inception  ... 
doi:10.9734/ajrcos/2021/v10i230239 fatcat:g5v6pxw375bozfimiekxbo4aya
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