Fuzzy Segmentation Research Articles

Robust Suppressed Competitive Picture Fuzzy Clustering Driven by Entropy

In the fuzzy clustering process, the clustering number for image or text data to be classified is not easy to determine or unknown. The competitive learning algorithm can automatically determine the optimal clustering number to avoid the problem of inappropriate artificial selection. In this paper, based on the clustering by competitive agglomeration (CA), the idea of the “Competitive Learning Mechanism” is introduced to picture fuzzy clustering to obtain the competitive agglomeration picture fuzzy clustering (CAPFCM). The competitive learning regular term of the CAPFCM objective function is reinterpreted from the perspective of minimizing the entropy, and the general framework of the entropy competitive clustering algorithm is constructed. Moreover, the competitive learning regular term of the objective function is replaced by quadratic entropy, Renyi entropy or Shannon entropy to obtain different entropy competitive clustering. To improve the efficiency of the CAPFCM algorithm, the suppressed factor is introduced to appropriately increase the maximum value of the picture fuzzy partition information for different clusters and suppress all others. In addition, this paper proposes a robust adaptive entropy competitive picture fuzzy clustering segmentation algorithm with neighborhood spatial information constraints to enhance the anti-noise ability of the picture fuzzy clustering algorithm for noise image. Experiments show that robust CAPFCM can automatically determine the clustering number and greatly improve the operation efficiency and segmentation performance.

SuFMoFPA: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images

Coronavirus disease 2019 or COVID-19 is one of the biggest challenges which are being faced by mankind. Researchers are continuously trying to discover a vaccine or medicine for this highly infectious disease but, proper success is not achieved to date. Many countries are suffering from this disease and trying to find some solution that can prevent the dramatic spread of this virus. Although the mortality rate is not very high, the highly infectious nature of this virus makes it a global threat. RT-PCR test is the only means to confirm the presence of this virus to date. Only precautionary measures like early screening, frequent hand wash, social distancing use of masks, and other protective equipment can prevent us from this virus. Some researches show that the radiological images can be quite helpful for the early screening purpose because some features of the radiological images indicate the presence of the COVID-19 virus and therefore, it can serve as an effective screening tool. Automated analysis of these radiological images can help the physicians and other domain experts to study and screen the suspected patients easily and reliably within the stipulated amount of time. This method may not replace the traditional RT-PCR method for detection but, it can be helpful to filter the suspected patients from the rest of the community that can effectively reduce the spread in the of this virus. A novel method is proposed in this work to segment the radiological images for the better explication of the COVID-19 radiological images. The proposed method will be known as SuFMoFPA (Superpixel based Fuzzy Modified Flower Pollination Algorithm). The type 2 fuzzy clustering system is blended with this proposed approach to get the better-segmented outcome. Obtained results are quite promising and outperforming some of the standard approaches which are encouraging for the practical uses of the proposed approach to screening the COVID-19 patients.

Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method

Background and ObjectiveBrain tumor segmentation is a challenging issue due to noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual MRI segmentation is a very tedious, time-consuming, and user-dependent task. This paper aims to presents a novel level set method to address aforementioned challenges for reliable and automatic brain tumor segmentation. MethodsIn the proposed method, a new functional, based on level set method, is presented for medical image segmentation. Firstly, we define a superpixel fuzzy clustering objective function. To create superpixel regions, multiscale morphological gradient reconstruction (MMGR) operation is used. Secondly, a novel fuzzy energy functional is defined based on superpixel segmentation and histogram computation. Then, level set equations are obtained by using gradient descent method. Finally, we solve the level set equations by using lattice Boltzmann method (LBM). To evaluate the performance of the proposed method, both synthetic image dataset and real Glioma brain tumor images from BraTS 2017 dataset are used. ResultsExperiments indicate that our proposed method is robust to noise, initialization, and intensity non-uniformity. Moreover, it is faster and more accurate than other state-of-the-art segmentation methods with the averages of running time is 3.25 seconds, Dice and Jaccard coefficients for automatic tumor segmentation against ground truth are 0.93 and 0.87, respectively. The mean value of Hausdorff distance, Mean absolute Distance (MAD), accuracy, sensitivity, and specificity are 2.70, 0.005, 0.9940, 0.9183, and 0.9972, respectively. ConclusionsOur proposed method shows satisfactory results for Glioma brain tumor segmentation due to superpixel fuzzy clustering accurate segmentation results. Moreover, our method is fast and robust to noise, initialization, and intensity non-uniformity. Since most of the medical images suffer from these problems, the proposed method can more effective for complicated medical image segmentation.

Analytical fuzzy space geometry I

In this paper, we introduce a few basic concepts of fuzzy space geometry in the three-dimensional Euclidean space. The ideas that we study here are space fuzzy points, distance between two space fuzzy points, and space fuzzy line segments. To represent a space fuzzy point, we introduce the idea of a reference function of three variables. Accordingly, we define an S-type space fuzzy point. The concepts of same points and inverse points with respect to two continuous space fuzzy points are studied to formulate the fuzzy space geometrical concepts. To formulate same and inverse points for space fuzzy points, we provide the concepts of a fuzzy number along a line in the space. With the help of the introduced three-variable reference function and fuzzy number along a line, explicit general expressions of same and inverse points for space fuzzy points are provided. Employing the concept of inverse points, we define a fuzzy distance between two space fuzzy points. Using the idea of the same points, addition and convex combination of two space fuzzy points are defined. A fuzzy line segment is formulated by a convex combination of two space fuzzy points. In the sequel, a concept of coincidence of two space fuzzy points is also provided. All the provided ideas are supported with numerical examples and necessary pictorial illustrations. Importantly, we also provide algorithms to find(i)the fuzzy distance between two space fuzzy points,(ii)the membership value of a number in the fuzzy distance between two space fuzzy points and(iii)the membership value of a point in the space fuzzy line segment.

A medical analytical system using intelligent fuzzy level set brain image segmentation based on improved quantum particle swarm optimization

Medical image segmentation demonstrates a significant part in curative image exploration and dispensation, is a multifaceted and perplexing assignment for reckoning efficiency and dissection precision. Segmenting an image is essential to dissection different components of the image, which is prominent fact to identify region of defect accurately. An Intelligent Fuzzy Level Set Method (IFLSM) along with an over-all search proficiency of Improved Quantum Particle Swarm Optimization (IQPSO) for image segmentation is proposed to improve the steadiness and meticulousness thus aiming at reduction of opening sensitivity. The proposed algorithm aims at optimizing the opening contours by utilizing the IQPSO method in addition with intelligent fuzzy clustering method, and segments the image using enhanced Level Set Method (LSM). A stable cluster head is identified using the comprehensive quest aptitude of IQPSO. The iteration period will also provide a pre-segmentation contour which is nearer to Region of Interest (ROI). The implementation of the proposed work for segmenting brain tissues through Magnetic Image Resonance (MRI) images provides an optimized result which is 15% more than the original FLSM algorithm. The obtained contours from the proposed work shows more stability than the original FLSM. The proposed work shows a promising significant improvement in the image segmentation process.

CSFCM: An improved fuzzy C-Means image segmentation algorithm using a cooperative approach

Fuzzy c-means (FCM) is one of the most widely used classification algorithms specially in image segmentation. Like any algorithm, FCM has some drawbacks such as the choice of the number of clusters and the cluster’s center initialization. In this work, we propose new approaches to deal with these two drawbacks. We propose for the first problem two approaches. The first proposed approach exploits neural networks and the Xie and Beni index, while the second one exploits the histogram. Concerning the second problem, we propose a new metaheuristics cooperation approach using the Genetic Algorithm (GA), Biogeography Based Algorithm(BBO), and Firefly Algorithm (FA). This cooperation is managed by a multi-agent system allowing to determine automatically the fittest metaheuristics parameters. Finally, we propose to use a histogram-based version of FCM to reduce the execution time of the algorithm. Experimental results show that our proposed approach improves the performance of the basic FCM algorithm and outperforms other methods proposed in the literature.

Blood cell image segmentation using modified fuzzy divergence with morphological transforms

Blood cell images contain lots of uncertainties due to the randomness and variability of gray values. The contrast between the objects and the background of blood cell images are viewed as fuzzy in nature. So, varieties of fuzzy image thresholding mechanisms have been adopted for removing the grayness and vagueness of blood cell images. In this paper, a noble fuzzy segmentation technique is proposed, which processes fuzzy divergence with the mathematical morphological operation. At first, blood cell images are pre-processed to avoid uneven illumination and to reduce noise by fuzzy image morphology, and in the later stage a suitable threshold point is determined using the modified fuzzy divergence. The quality of the obtained images in the fuzzy divergence with the morphology method is observed for enhancement and better entropy. In this paper, we have used a new fuzzy Lomax-Gumbel membership function with the Fuzzy Divergence algorithm in order to overcome the loopholes of other well-known membership functions. The experimental results tested form the point of view of the robustness of the modified method by comparing with Chaira’s method. Finally, the mean value, entropy, and Standard Deviation have been calculated, which are found as greater values than the values of Chaira’s method.

A fuzzy segmentation analysis of airline passengers in the U.S. based on service satisfaction.

This study investigates airline passenger satisfaction and service quality in the U.S. market, employing fuzzy logic and fuzzy segmentation methods. Elasticities are then determined to evaluate the sensitivity of satisfaction based on service quality attributes. The survey was developed consistent with functional and technical quality and from existing airline service quality and satisfaction literature. Data were collected online using Amazon Mechanical Turk, resulting in 624 respondents. The results show that both technical and functional quality play a role in determining satisfaction with airlines, though passengers are more satisfied with functional quality than technical quality. Overall, a majority of airline passengers are indifferent; they are neither satisfied nor unsatisfied toward airline service quality. Additionally, this research introduced additional factors in the analysis that have not been previously researched laying the groundwork for future research.

RETRACTED ARTICLE: Analysis of basic neural network types for automated skin cancer classification using Firefly optimization method

In recent days, cancer is a deadly disease because of its spreading nature to other cells, and this disease is not identified at an early detection stage. Generally, the cancer is detected with the help of a biopsy method, which is a painful approach. Due to the development of technology, nowadays, it is identified with the help of image processing methods. Here, the image processing approach is used for identifying and classifying the skin cancer types, namely melanoma, common and atypical nevi. The methods used earlier for the detection and classification are artificial skin leison merging, Raman spectroscopy and back-propagation networks. Cancer is classified into many types like blood cancer, bone, colon, and stomach and skin cancer. Among these cancer types, skin cancer can be a dreadful disease, which is detected and then treated at the starting stage of the disease. Hence, this paper proposed an optimized neural and fuzzy approach for skin cancer classification. The fuzzy c-means segmentation is used for the detection of the cancer region. Firefly optimization determines the dominant feature for the training of the neural network. The dominant feature is determined by reducing the error rate of the classifier. The overall process is evaluated with the help of evaluation metrics like accuracy, specificity and sensitivity. In this proposed method, the best result is achieved for the pattern net by improving its accuracy by 4.9% from its previous Moth-Flame Optimization based classification in its evaluation.

A fuzzy segmentation study of gastronomical experience

Gastronomy is an essential component of cultural heritage for tourists. Culinary pleasures become motivations that determine the choice of destination and, at the same time, an essential variable for the composition of tourist satisfaction in the travelling experience. The aim of the study is to analyse the gastronomic experiences of the foreign visitors (tourists and excursionists) who visit Córdoba, a remarkable World Heritage Site of Spain, through a fuzzy clustering segmentation method. The results of this research highlight the existence of three tourist segments named as foodies, non-foodies and intermediaries on the basis of their perceptions regarding the gastronomic experiences of the city. We conclude that the previous knowledge of local dishes such as Salmorejo, Rabo de Toro y Flamenquín is one of the main determinants to have a higher probability to belong to the ‘foodies’ segment, meanwhile the lack of knowledge also increases the probability to belong ‘the no-foodies’ segment. Interesting insights for policy makers, destination marketers and restaurateurs are discussed.

Novel coupled DP system for fuzzy C-means clustering and image segmentation

This study proposed a novel fuzzy c-means clustering method which calculates the density of the data points based on the weighted mean distance (WMFCM). A novel coupled DNA-GA and P system (DP system) is introduced to realize the clustering process. The evolutional rules in the coupled DP system can help the WMFCM algorithm jump out of the local optimum and get the initial clustering centers. The performance of the coupled DP system in dealing with fuzzy clustering problems is measured by conducting experimental analysis on UCI datasets and BSDS300 image datasets. And the experimental results are compared with several popular algorithms. Experimental results show that our algorithm can perform better than other algorithms.

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints.

This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback–Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means (FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model (SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272–12.9803 dB, 1.5501–13.4396 dB, 1.9113–11.2613 dB and 1.0233–10.2804 dB over the other methods, and the Misclassification rate (MSR) decreases by 0.32–37.32%, 5.02–41.05%, 0.3–21.79% and 0.9–30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation.

Analysis of Rice Ageing Process Using Fuzzy and Graph Cut

Rice botanically belongs to Oryza sativa L. of Gramineae family. Rice is the significant principal foods for almost majority of the world’s population and impacts the livelihood and economy of many billion people. Though there are many well established techniques such as electronic devices, sensors, biosensors or high end instruments to study the different chemical components, sample preparation, specificity, sensitivity, accuracy and reusable issues. In order to overcome such issues, an alternate technique or method need to be developed which can replace the human intervention to avoid the experimental errors through analyzing the surface structure rather than the chemical method. Among the different changes that occurred during the ageing process, internal structure is also an important phenomenon because of the starch modification with several factors like temperature, moisture content and storage period. Hence, the objective of the study concentrates on the analyzing the structural changes to assess the age of rice through fuzzy and graph cut segmentation technique.

Total Bregman divergence-based fuzzy local information C-means clustering for robust image segmentation

The fuzzy local information C-means clustering algorithm (FLICM) is an important robust fuzzy clustering segmentation method, which has attracted considerable attention over the years. However, it lacks certain robustness to high noise or severe outliers. To improve the accuracy and robustness of the FLICM algorithm for images corrupted by high noise, a novel fuzzy local information c-means clustering utilizing total Bregman divergence (TFLICM) is proposed in this paper. The total Bregman divergence is modified by the local neighborhood information of sample to further enhance the ability to suppress noise, and then modified total Bregman divergence is introduced into the FLICM to construct a new objective function of robust fuzzy clustering, and the iterative clustering algorithm with high robustness is obtained through optimization theory. The convergence of the TFLICM algorithm is proved by the Zangwill theorem. In addition, the validity of the TFLICM algorithm applied in noise image segmentation is explained by means of sample weighting fuzzy clustering. Meanwhile, the generalized total Bregman divergence unifies the Bregman divergence with the total Bregman divergence and enhances the universality of the TFLICM algorithm applied in segmenting complex medical and remote sensing images. Some experimental results show that the TFLICM algorithm can obtain better segmentation quality and stronger anti-noise robustness than the existing FLICM algorithm.

A Fuzzy Segmentation Method to Learn Classification of Mitosis

Mitotic counts are widely used as a metric for cellular proliferation for prognosis and to determine the aggressiveness of individual cancers. This study presents a less labor-intensive method to count mitotic cells in breast cell sections. The proposed algorithm involves two phases: candidate segmentation and detection. During candidate segmentation, images are filtered through a blue ratio threshold to remove unnecessary background information and to increase the color difference between targets and non-targets for an entire digitized image. A fuzzy candidate segmentation method is used to adaptively determine threshold values in order to dichotomize gray-level images and distinguish the images of mitotic candidates from the background. The thresholding scheme integrates the spatial characteristics’ distribution in a histogram to determine an intensity threshold for the processed image, in order to filter insignificant information. During the detection phase, a two-class classification uses an attention mechanism that is realized by a set of fully connected neural networks, instead of convolutional layers, which decreases the computational cost. The validation test using ICPR2012 competition datasets shows that the proposed model outperforms current state-of-art techniques, in terms of the metrics, Accuracy, F1-score, and Precision and Recall.

Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching

In order to overcome shortcomings of the traditional fuzzy clustering algorithm for image segmentation, such as that are easily affected by noise, sensitive to the initial value of clustering center, easily falling into local optimum, and inadequate ability of fuzzy information processing, an intuitionistic fuzzy clustering image segmentation algorithm is proposed based on flower pollination optimization with nearest neighbor searching. Firstly, a novel extraction strategy of image spatial information is proposed, and then an intuitionistic fuzzy clustering objective function with image spatial information is constructed to improve the algorithm’s robustness against noise and enhance the ability of the algorithm to process the image fuzzy information. In order to overcome the defects of sensitivity to clustering centers and easily falling into local optimum, a flower pollination algorithm based on nearest neighbor learning search mechanism is proposed. Experimental results show that the proposed method can get satisfactory segmentation results on a variety of noisy images.

Adaptive G–G clustering for fuzzy segmentation of multivariate time series

In this paper, Gath–Geva (G–G) fuzzy clustering is extended to adaptively segment hydrometeorological multivariate time series. First, KPCA is used to extract principle components of multivariate time series to remove the impacts of redundant and irrelevant variables. Then, taking the time information into account, the segmentation of principle components of multivariate time series is derived with the modified Davies–Bouldin Index and adaptive G–G fuzzy clustering. In the experiment, our proposed algorithm is applied on the real-world hydrometeorological multivariate time series collected every 6 min with length $$N=720$$ . Comparison with the existing segmentation algorithms, our proposed algorithm proves the applicability and usefulness in hydrometeorological multivariate time series analysis.

A dynamic interval type-2 fuzzy customer segmentation model and its application in E-commerce

Internet-based services and retail are growing rapidly. To manage online customer relationships with linguistic comments, we propose a dynamic interval type-2 fuzzy customer segmentation model. Interval type-2 fuzzy linguistic labels (IT2FLLs) are used to model customer comments. The similarity of IT2FLLs is computed based on an extended distance method. Customers are segmented dynamically according to the fuzzy equivalence relations of similarity. A case study in E-commerce shows the application of the proposed model, and a comparative analysis shows its effectiveness. The dynamic customer segmentation can help managers to have a deep understanding on customers’ purchasing behaviors and to make accurate recommendations.

A New Validity Index in Overlapping Clusters for Medical Images

Detecting automatically overlapping structures is a major issue in segmentation. In addition, the assessment of the quality of the clusters produced by fuzzy segmentation algorithms is one of the challenging tasks in segmentation process. To address this issue, a wide variety of functions called validity indexes have been proposed and developed. The overlap phenomenon constitute a source of failure for most of these indexes, in this context we propose a new cluster validity index VECS to recognize the optimal number of clusters adapted to the fuzzy c-means algorithm; based on the entropy, on the partition coefficient functions and on two criteria: the compactness within the classes and the inter-classes separation. We conducted experiments on medical imaging data sets, including simulated brain magnetic resonance imaging (MRI) and leukemia images. The experimental results reveal that, our VECS validity index outperform the other existing methods and has greater ability to identify the appropriate number of segments on high overlapped data sets.

Spatial possibilistic fuzzy C-Mean segmentation method integrated with brain Mid-Sagittal Surface information extracted by an evolutionary algorithm

Spatial possibilistic fuzzy C-Mean segmentation method integrated with brain Mid-Sagittal Surface information extracted by an evolutionary algorithm