Fuzzy Segmentation Algorithm Research Articles

Design of Time Series Fuzzy Segmentation Algorithm for Communication Data Classification

In order to solve the problem that the imbalance of communication data sets leads to a significant increase in classification difficulty, a classification algorithm for fuzzy segmentation of time series is proposed. The principal component analysis method is used to obtain the eigenvector with the largest eigenvalue. The time series of data is established according to the interval number theory. The segmentation target of the communication data time series is characterized by the Langmuir distance measurement function between data and categories. The classification result of fuzzy segmentation is obtained based on the judgment relationship between the difference of fuzzy classification matrix and the closing condition. The experimental results show that the number of data in the three classification situations of the algorithm in this paper is always at the corresponding ideal level, with high accuracy and low error and failure.

Online Measurement of Melt-Pool Width in Direct Laser Deposition Process Based on Binocular Vision and Perspective Transformation.

Direct laser deposition (DLD) requires high-energy input and causes poor stability and portability. To improve the deposited layer quality, conducting online measurements and feedback control of the dimensions, temperature, and other melt-pool parameters during deposition is essential. Currently, melt-pool dimension measurement is mainly based on machine vision methods, which can mostly detect only the deposition direction of a single melt pool, limiting their measurement range and applicability. We propose a binocular-vision-based online measurement method to detect the melt-pool width during DLD. The method uses a perspective transformation algorithm to align multicamera measurements into a single-coordinate system and a fuzzy entropy threshold segmentation algorithm to extract the melt-pool true contour. This effectively captures melt-pool width information in various deposition directions. A DLD measurement system was constructed, establishing an online model that maps the melt-pool width to the offline deposited layer width, validating the accuracy of the binocular vision system in measuring melt-pool width at different deposition angles. The method achieved high accuracy for melt-pool measurements within certain deposition angle ranges. Within the 30°-60° measurement range, the average error is 0.056 mm, with <3% error. The proposed method enhances the detectable range of melt-pool widths, improving cladding layers and parts.

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

A segmentation method based on the deep fuzzy segmentation model in combined with SCANDLE clustering

To enhance the low clustering accuracy of the fuzzy clustering segmentation algorithm for analyzing high spatial resolution remote sensing images (HSRRSIs), a deep fuzzy segmentation model (DFSM)combined with Spectral Clustering with Adaptive Neighbors for Deep Learning (SCANDLE) clustering is proposed. The DFSM is used to over-segment the image, and the automatic coding structure is used to adaptively fuse the image features, minimizing the internal compactness and maximizing the external separability of the clustering, yielding better results. Meanwhile, the SCANDLE clustering model is used to cluster the over-segmentation results, and the matrix construction algorithm for adaptive neighborhood allocation is used to map the frame of the connected layer and optimally combine the over-segmentation images to realize the final segmentation results. The new method can accurately segment HSRRSIs with good segmentation performance.

Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation

A fuzzy C-Means segmentation algorithm can be implemented in an image segmentationbased on the Mahalanobis distance; However, this method only needs to consider the colorspace situation, not the neighborhood system of the image. It was an effective edge detectionprocess unwell performed and generated less accuracy in segmentation results. In this article,we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatialinformation (MFCMS). The proposed method combines feature space and images of theinformation of the neighborhood (spatial information) to improve the accuracy of the result ofsegmentation on the image. The MFCMS consists of two steps, the histogram threshold modulefor the first step and the MFCMS module for the second step. The Histogram Threshold moduleis used to get the MFCMS initialization conditions for the cluster centroid and the number ofcentroids. Test results show that this method provides better segmentation performance thanclassification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,respectively.

Interval type-2 possibilistic fuzzy clustering noisy image segmentation algorithm with adaptive spatial constraints and local feature weighting & clustering weighting

The interval type-2 possibilistic fuzzy C-means (IT2PFCM) algorithm is a popular data clustering and image segmentation method. However, the algorithm fails to consider the spatial information of the image and has limited accuracy for noisy images. The other problem of the IT2PFCM algorithm assumes that all features are equally important in the segmentation process and perform less well as it segments color images. In addition, the IT2PFCM algorithm produces overlapping clusters and even misses some clusters if the groups contained in the data are relatively close to each other. We propose an improved IT2PFCM algorithm for noisy image segmentation to solve the problems. Firstly, an adaptive spatial constraint method is proposed to decrease the algorithm's sensitivity to noise. Secondly, we utilize an automatic local feature weighting and clustering weighting scheme to better segment color images and eliminate overlapping clusters. Finally, an objective function with adaptive spatial constraint, local feature weighting, and clustering weighting is constructed for image segmentation. Extensive experiments on synthetic and natural images show that the proposed algorithm outperforms the state-of-the-art algorithms. The convergence proof of the proposed algorithm is also provided.

Segmentation of acetowhite region in uterine cervical image based on deep learning

Acetowhite (AW) region is a critical physiological phenomenon of precancerous lesions of cervical cancer. An accurate segmentation of the AW region can provide a useful diagnostic tool for gynecologic oncologists in screening cervical cancers. Traditional approaches for the segmentation of AW regions relied heavily on manual or semi-automatic methods. To automatically segment the AW regions from colposcope images. First, the cervical region was extracted from the original colposcope images by k-means clustering algorithm. Second, a deep learning-based image semantic segmentation model named DeepLab V3+ was used to segment the AW region from the cervical image. The results showed that, compared to the fuzzy clustering segmentation algorithm and the level set segmentation algorithm, the new method proposed in this study achieved a mean Jaccard Index (JI) accuracy of 63.6% (improved by 27.9% and 27.5% respectively), a mean specificity of 94.9% (improved by 55.8% and 32.3% respectively) and a mean accuracy of 91.2% (improved by 38.6% and 26.4% respectively). A mean sensitivity of 78.2% was achieved by the proposed method, which was 17.4% and 10.1% lower respectively. Compared to the image semantic segmentation models U-Net and PSPNet, the proposed method yielded a higher mean JI accuracy, mean sensitivity and mean accuracy. The improved segmentation performance suggested that the proposed method may serve as a useful complimentary tool in screening cervical cancer.

Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking

The Fuzzy C-means (FCM) clustering algorithm is an effective method for image segmentation. Non-local spatial information considers more redundant information of the image thus is more robust to noise. However, under-segmentation of non-local spatial information may exist with higher noise density. The number of iteration steps is also significant in FCM, and employing membership linking can effectively reduce the number of iteration steps. Nonetheless, when there are outliers in the membership degree, the membership linking can make the algorithm converge prematurely before reaching the optimum, affecting segmentation performance. This paper presents a fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking (FSC_LNML). Firstly, local variance templates are utilized to eliminate the under-segmentation of non-local information, and local variance & non-local information are integrated into the FCM objective function to improve robustness. Secondly, the mean membership linking is employed as the denominator of the objective function to reduce the number of iterations and solve the problem that the algorithm converges early before reaching the optimum when the membership has an outlier. Thirdly, the absolute intensity difference between the original image and the local variance & non-local information and its inverse are used to adaptively constrain the original image and the local variance & non-local information. Finally, the concept of the subspace is introduced to adaptively assign appropriate weights to each dimension of the image to improve the segmentation performance of color images. The simulation results on noisy grayscale images and noisy color images show that the efficiency of the proposed method FSC_LNML is better than other fuzzy-based clustering algorithms. The convergence proof of the algorithm is also presented.

Fuzzy segmentation and black widow-based optimal SVM for skin disease classification.

The skin, which has seven layers, is the main human organ and external barrier. According to the World Health Organization (WHO), skin cancer is the fourth leading cause of non-fatal disease risk. In medicinal fields, skin disease classification is a major challenging issue due to inaccurate outputs, overfitting, larger computational cost, and so on. We presented a novel approach of support vector machine-based black widow optimization (SVM-BWO) for skin disease classification. Five different kinds of skin disease images are taken such as psoriasis, paederus, herpes, melanoma, and benign with healthy images which are chosen for this work. The pre-processing step is handled to remove the noises from the original input images. Thereafter, the novel fuzzy set segmentation algorithm subsequently segments the skin lesion region. From this, the color, gray-level co-occurrence matrix texture, and shape features are extracted for further process. Skin disease is classified with the usage of the SVM-BWO algorithm. The implementation works are handled in MATLAB-2018a, thereby the dataset images were collected from ISIC-2018 datasets. Experimentally, various kinds of performance analyses with state-of-the-art techniques are performed. Anyway, the proposed methodology outperforms better classification accuracy of 92% than other methods. Workflow diagram of the proposed methodology.

Research on sludge concentration detection based on image processing

Sludge concentration is an important indicator of water quality. Based on the different light reflection characteristics of water and sludge, this paper studies the detection of sludge concentration using image processing technology. Combined with MATLAB tools, this paper uses the probability based illumination nonuniformity correction algorithm and adaptive fuzzy threshold segmentation algorithm to simulate and analyze the sewage image, extract the area characteristics and calculate the sludge concentration. At the same time, compared with the real value, it is concluded that the image processing technology can accurately detect the sludge concentration, which lays the foundation for the solid-liquid separation research.

Multi-Feature Analysis for Automated Brain Stroke Classification Using Weighted Gaussian Naïve Bayes Classifier

In today’s world, brain stroke is considered as a life-threatening disease provoked by undesirable blockage among the arteries feeding the human brain. The timely diagnosis of this brain stroke detection in Magnetic Resonance Imaging (MRI) images increases the patient’s survival rate. However, automated detection plays a significant challenge owing to the complexity of the shape, dimension of size and the location of stroke lesions. In this paper, a novel optimized fuzzy level segmentation algorithm is proposed to detect the ischemic stroke lesions. After segmentation, the multi-textural features are extracted to form a feature set. These features are given as input to the proposed weighted Gaussian Naïve Bayes classifier to discriminate normal and abnormal stroke lesion classes. The experimental result manifests that the proposed methodology achieves a higher accuracy as compared with the existing state-of-the-art techniques. The proposed classifier discriminates normal and abnormal classes efficiently and attains 99.32% of accuracy, 96.87% of sensitivity and 98.82% of F1 measure.

Noise distance driven fuzzy clustering based on adaptive weighted local information and entropy-like divergence kernel for robust image segmentation

Kernel method is an effective way to solve the problem of nonlinear mode analysis, and its key is the selection or construction of kernel function. This paper firstly induced entropy-like divergence by combining Jensen-Shannon/Bregman divergence with convex function, its mercer kernel function called entropy-like divergence kernel is also constructed. Secondly, an adaptive noise distance based on entropy-like divergence kernel and a novel fuzzy weighted local factor of robust fuzzy clustering are presented, and they are also embedded into the objective function of fuzzy C-means clustering with noise cluster. In the end, a novel noise-resistant fuzzy weighed local information clustering based on entropy-like divergence kernel (NEKWFLICM) is proposed, and its convergence is strictly proved by convergence theorem of alternating iteration. Many experimental results delicate that the proposed algorithm has more robust and accurate than a series of existing state-of-the-art Gaussian kernel-based fuzzy clustering-related segmentation algorithms in the presence of high noise.

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.

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.

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.

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.

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model.

Since the fuzzy local information C-means (FLICM) segmentation algorithm cannot take into account the impact of different features on clustering segmentation results, a local fuzzy clustering segmentation algorithm based on a feature selection Gaussian mixture model was proposed. First, the constraints of the membership degree on the spatial distance were added to the local information function. Second, the feature saliency was introduced into the objective function. By using the Lagrange multiplier method, the optimal expression of the objective function was solved. Neighborhood weighting information was added to the iteration expression of the classification membership degree to obtain a local feature selection based on feature selection. Each of the improved FLICM algorithm, the fuzzy C-means with spatial constraints (FCM_S) algorithm, and the original FLICM algorithm were then used to cluster and segment the interference images of Gaussian noise, salt-and-pepper noise, multiplicative noise, and mixed noise. The performances of the peak signal-to-noise ratio and error rate of the segmentation results were compared with each other. At the same time, the iteration time and number of iterations used to converge the objective function of the algorithm were compared. In summary, the improved algorithm significantly improved the ability of image noise suppression under strong noise interference, improved the efficiency of operation, facilitated remote sensing image capture under strong noise interference, and promoted the development of a robust anti-noise fuzzy clustering algorithm.

Unsupervised fuzzy model-based image segmentation

This paper presents a novel unsupervised fuzzy model-based image segmentation algorithm. The proposed algorithm integrates color and generalized Gaussian density (GGD) into the fuzzy clustering algorithm and incorporates their neighboring information into the learning process to improve the segmentation accuracy. In addition, a membership entropy term is used to make the algorithm not sensitive to initial clusters. To optimize the objective function of the proposed segmentation model, we define the dissimilarity measure between GGD models using the Kullback–Leibler divergence, which evaluates their discrepancy in the space of generalized probability distributions via only the model parameters. We also present mathematical analysis that proves the existence of the cluster center for the GGD parameters, thus establishing a theoretical basis for its use. Experimental results show that our proposed method has a promising performance compared with the current state-of-the-art fuzzy clustering-based approaches.

Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images.

Medical image analysis plays an important role in computer-aided liver-carcinoma diagnosis. Aiming at the existing image fuzzy clustering segmentation being not suitable to segment CT image with non-uniform background, a fast robust kernel space fuzzy clustering segmentation algorithm is proposed. Firstly, the sample in euclidean space is mapped into the high dimensional feature space through the kernel function. Then the linear weighted filtering image is obtained by combining the current pixel with its neighborhood pixels through the space information in CT image. Finally, the two-dimensional histogram between the clustered pixel and its neighborhood mean is introduced into the robust kernel space image fuzzy clustering, and the iterative expression of the fast robust fuzzy clustering in kernel space is obtained by using Lagrange multiplier method. The experimental results on four databases show that our proposed method can segment liver tumors from abdominal CT volumes effectively and automatically, and the comprehensive segmentation performance of the proposed method is superior to that of several existing methods.

Quantification of Epicardial and Thoracic Adipose Tissue using WOA Optimized CNN

Cardiac fat depots are associated with the heart diseases. Epicardial fat and thoracic fat plays the major role in the development of cardiovascular disease. The increased thickness of the epicardial and thoracic fat leads to several diseases such as metabolic syndrome, coronary atherosclerosis, etc. It is necessary to quantify the epicardial adipose tissue and thoracic adipose tissue. There are different imaging and assessing techniques for epicardial and thoracic adipose tissue quantification. These tissues can be quantified automatically or manually from the CT and MRI cardiac scans. The quantification of the epicardial fat and thoracic fat requires segmentation of these fats by various segmentation methods and then they are quantified. This project proposes the fully automatic segmentation and quantification of the epicardial and thoracic adipose tissues from the cardiac CT scan images using the krill herd optimization algorithm and fuzzy c-means segmentation algorithm. The whale optimization algorithm performs the feature selection process. The fuzzy cmeans algorithm is used for the segmentation process by means of clustering which segments the epicardial fat and paracardial adipose tissue(EAT &amp;PAT) from the input image. The segmented epicardial and paracardial fat region are then used for the quantification process which provides the epicardial and thoracic fat volume. The thoracic fat is the combination of the epicardial and paracardial fat. This proposed system is implemented by using the MATLAB code. The proposed system is simple, fully automatic and produces accurate results.