Fuzzy Local Information C-means Clustering Research Articles

Prediction Models Applying Convolutional Neural Network based Deep Learning to Cervical Cancer Outcomes

Early identification of Cervical Cancer (CC) helps in reducing the death rate of CC patients. A Pap smear/Pap test, which is also a widely used method, is used to detect CC in its early stages. The automatic detection and categorization of Pap smear images is a difficult task. So, it is important to design and implement a high-efficiency with low-cost screening system. Therefore, this paper compares the efficiency of two deep learning-based cervical cancer screening strategies. The first technique taken for comparison is the Mutation based Atom Search Optimization (MASO) optimized DenseNet 121 architecture. The MASO technique is used to adjust hyperparameters such as learning rate value, batch sizes, and the number of neurons in the dense layer in the DenseNet 121 architecture. The second technique taken is the Sooty Tern optimized CNN-based Long Short-Term Memory (ST-CNN-LSTM) classifier to identify the different types of cervical cancer stages. The areas affected by the cancer are identified using Kernel-Weighted Fuzzy Local Information C-Means clustering (KWFLICM) model. This model removes the nucleus and cytoplasm from the background area of the cell. Normal, mild dysplastic, severe dysplastic, and carcinoma are among the CC classifications offered by these two classifiers. The efficiency of these classifiers is compared in two datasets (Hervel and SIPaKMeD) via different performance metrics such as specificity, precisions, F-score, accuracy, confusion matrix, sensitivity, and recall. The ST-CNN-LSTM offers improved performance in terms of accuracy (99.80%), sensitivity (98.83%), and specificity (99%) when compared to other classifiers.

FLICM clustering with matrix factorization based course recommendation in an E-learning platform

Technology-enabled learning has progressively grown for research areas with wide application of information and communication technologies for numerous standard-compliant Learning and Open Educational Resources. This provides formidable support to users for the selection of courses when they want to develop the course with available learning materials. But selecting a course via searching learning objects is an inherently complex operation having various repositories. In an E-learning Platform, many complexities arise due to various software tools and specification formats that hinder the success of the course. In this paper, many obstacles in the E-learning platform are eradicated by utilizing Fuzzy Local Information C-Means (FLICM) clustering with matrix factorization for the selection of courses. The dataset utilized in this work is E-Khool review data, from which an agglomerative matrix is generated. Here, the agglomerative matrix consists of the learner series matrix and course series matrix along with their binary matrix. After this process, course grouping is carried out by FLICM clustering with matrix factorization. Moreover, group course bilevel matching, followed by relevant learner retrieval and group user is done by Minkowski and Chebyshev distance. From this learner’s preferred course is retrieved and then a recommendation using matrix factorization is carried out. Finally, the course is recommended for the user based on maximum rating. Furthermore, the performance of developed FLICM_matrix factorization is achieved by performance metrics, like precision, recall, and f-measure with values 0.915, 0.850, and 0.882, accordingly.

Detection of changes in synthetic aperture radar images using Modified Gauss-Log ratio and Fuzzy Local Information C-Means clustering

Detection of changes in synthetic aperture radar images using Modified Gauss-Log ratio and Fuzzy Local Information C-Means clustering

An Algorithm to Extract the Boundary and Center of EUV Solar Image Based on Sobel Operator and FLICM

An algorithm to extract the disk boundary and center of EUV solar image using the Sobel operator, Fuzzy Local Information C-Means Clustering algorithm (FLICM), and the least square circle fitting method is proposed in this paper. The Sobel operator can determine the solar disk boundary preliminarily, and then the image is processed further using the FLICM algorithm. After the background is removed based on the clustered image and the boundary points can be highlighted, these points are fitted using the least square circle fitting method as the final boundary circle. The solar data used in this paper was from the observation of the Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA) instrument. The 2523 19.3 nm solar images covering solar minimum, moderate solar activity, and more active suns were calculated using the proposed algorithm to analyze the accuracy statistically. The statistical comparison results demonstrate that the method is accurate and effective. This method can support the processing of solar EUV images and serve the operational system of a space weather forecast.

A modified fuzzy dual-local information c-mean clustering algorithm using quadratic surface as prototype for image segmentation

Single-valued point can not effectively describe the gray characteristic of the image region, which makes existing fuzzy clustering algorithms difficult to segment image with uneven illumination, weak edges and strong noise. Hence, fuzzy c-means clustering with quadratic polynomial surface as clustering center has become a new method to solve complex image segmentation. Aiming at the shortcoming of current latest spatial fuzzy c-means clustering algorithm with quadratic surface as clustering center, quadratic polynomial surface is firstly introduced into existing widely used fuzzy local information c-means clustering algorithm, and further modify the membership degree of fuzzy local information c-means clustering by means of local membership of neighborhood pixels, a novel fuzzy dual-local information c-means clustering with quadratic polynomial-center clusters is proposed to solve the segmentation problem of complex image in the presence of high noise. Then the convergence of the proposed algorithm is strictly proved by Zangwill theorem and bordered Hessian matrix. Experimental results show that the proposed algorithm has better segmentation performance and anti-noise robustness than existing quadratic polynomial-related QPFCM, widely used FLICM, and multiple state-of-the-art algorithms, meanwhile, it also promotes the development of robust fuzzy clustering-related image segmentation theory.

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Remote sensing image change detection is widely used in land use and natural disaster detection. In order to improve the accuracy of change detection, a robust change detection method based on nonsubsampled contourlet transform (NSCT) fusion and fuzzy local information C-means clustering (FLICM) model is introduced in this paper. Firstly, the log-ratio and mean-ratio operators are used to generate the difference image (DI), respectively; then, the NSCT fusion model is utilized to fuse the two difference images, and one new DI is obtained. The fused DI can not only reflect the real change trend but also suppress the background. The FLICM is performed on the new DI to obtain the final change detection map. Four groups of homogeneous remote sensing images are selected for simulation experiments, and the experimental results demonstrate that the proposed homogeneous change detection method has a superior performance than other state-of-the-art algorithms.

Rider Manta Ray Foraging Optimization-based Generative Adversarial Network and CNN feature for detecting glaucoma

Glaucoma is a leading and rapidly increasing disease in recent years, which affects the eye's optic nerve and causes everlasting vision loss. The early recognition of glaucoma is a significant one for decreasing the risk of permanent loss of sight. Therefore, it is necessary to detect glaucoma disease in the initial stage. Moreover, optic cup segmentation from retinal fundus images is an essential procedure for automatic glaucoma detection. In this paper, an efficient glaucoma detection approach is developed using Rider Manta-Ray Foraging Optimization-based General Adversarial Network ((Rider MRFO-based GAN). Here, the optic disc segmentation process is performed by the Fuzzy Local Information C-Means clustering (FLICM clustering). In addition, the sparking process is also employed in this glaucoma detection method for the blood vessel detection process. Several significant features in this detection model, namely mean, standard deviation, variance, kurtosis, skewness, entropy, and CNN features, are extracted to detect glaucomatous images further. The developed Rider MRFO approach is newly developed by the MRFO technique and Rider Optimization Algorithm (ROA). Additionally, the developed glaucoma detection technique performs better based on various parameters, like specificity, sensitivity, and accuracy. Hence, the developed Rider MRFO-based GAN model showed improved results with the highest accuracy of 0.96, the sensitivity of 0.94, and the specificity of 0.89.

Risk Factors of Restroke in Patients with Lacunar Cerebral Infarction Using Magnetic Resonance Imaging Image Features under Deep Learning Algorithm.

This study was aimed to explore the magnetic resonance imaging (MRI) image features based on the fuzzy local information C-means clustering (FLICM) image segmentation method to analyze the risk factors of restroke in patients with lacunar infarction. In this study, based on the FLICM algorithm, the Canny edge detection algorithm and the Fourier shape descriptor were introduced to optimize the algorithm. The difference of Jaccard coefficient, Dice coefficient, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), running time, and segmentation accuracy of the optimized FLICM algorithm and other algorithms when the brain tissue MRI images were segmented was studied. 36 patients with lacunar infarction were selected as the research objects, and they were divided into a control group (no restroke, 20 cases) and a stroke group (restroke, 16 cases) according to whether the patients had restroke. The differences in MRI imaging characteristics of the two groups of patients were compared, and the risk factors for restroke in lacunar infarction were analyzed by logistic multivariate regression. The results showed that the Jaccard coefficient, Dice coefficient, PSNR value, and SSIM value of the optimized FLICM algorithm for segmenting brain tissue were all higher than those of other algorithms. The shortest running time was 26 s, and the highest accuracy rate was 97.86%. The proportion of patients with a history of hypertension, the proportion of patients with paraventricular white matter lesion (WML) score greater than 2 in the stroke group, the proportion of patients with a deep WML score of 2, and the average age of patients in the stroke group were much higher than those in the control group (P < 0.05). Logistic multivariate regression showed that age and history of hypertension were risk factors for restroke after lacunar infarction (P < 0.05). It showed that the optimized FLICM algorithm can effectively segment brain MRI images, and the risk factors for restroke in patients with lacunar infarction were age and hypertension history. This study could provide a reference for the diagnosis and prognosis of lacunar infarction.

A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

Fuzzy clustering algorithm (FCM) can be directly used to segment images, it takes no account of the neighborhood information of the current pixel and does not have a robust segmentation noise suppression. Fuzzy Local Information C-means Clustering (FLICM) is a widely used robust segmentation algorithm, which combines spatial information with the membership degree of adjacent pixels. In order to further improve the robustness of FLICM algorithm, non-local information is embedded into FLICM algorithm and a fuzzy C-means clustering algorithm has local and non-local information (FLICMLNLI) is obtained. When calculating distance from pixel to cluster center, FLICMLNLI algorithm considers two distances from current pixel and its neighborhood pixels to cluster center. However, the algorithm gives the same weight to two different distances, which incorrectly magnifies the importance of neighborhood information in calculating the distance, resulting in unsatisfactory image segmentation effects and loss of image details. In order to solve this problem, we raise an improved self-learning weighted fuzzy algorithm, which directly obtains different weights in distance calculation through continuous iterative self-learning, then the distance metric with the weights obtained from self-learning is embedded in the objective function of the fuzzy clustering algorithm in order to improve the segmentation performance and robustness of the algorithm. A large number of experiments on different types of images show that the algorithm can not only suppress the noise but also retain the details in the image, the effect of segmenting complex noise images is better, and it provides better image segmentation results than the existing latest fuzzy clustering algorithms.

A novel kernelized total Bregman divergence-based fuzzy clustering with local information for image segmentation

Kernel weighted fuzzy local information c-means clustering (KWFLICM) is of great significance in the field of fuzzy clustering. Nevertheless, it is sensitive to high noise or outliers. Aiming at the shortcoming of KWFLICM algorithm, this paper proposes a novel fuzzy local information c-means clustering algorithm utilizing total Bregman divergence driven by polynomial kernel function (TKWFLICM). Firstly, total Bregman divergence (TBD) is kernelized by global polynomial kernel function to solve the problem of kernel-based distance metric without rotation-invariance. Secondly, TBD with polynomial kernel metric is modified by local neighborhood information of current pixel to further enhance the ability to suppress the noise in image. In the end, the modified TBD with polynomial kernel is introduced into the objective function of KWFLICM, and then a novel robust fuzzy clustering with high robustness is obtained. Experimental results show that the proposed algorithm performs better than existing state-of-the-art KWFLICM algorithm, and the kernelized TBD can also be utilized in fuzzy clustering-related algorithms, which will enhance the anti-noise robustness of these algorithms.

Reliable IoT-based Health-care System for Diabetic Retinopathy Diagnosis to defend the Vision of Patients

PurposeThe purpose of this paper is to design an Internet-of-Things (IoT) architecture-based Diabetic Retinopathy Detection Scheme (DRDS) proposed for identifying Type-I or Type-II diabetes and to specifically advise the Type-II diabetic patients about the possibility of vision loss.Design/methodology/approachThe proposed DRDS includes the benefits of automatic calculation of clip limit parameters and sub-window for making the detection process completely adaptive. It uses the advantages of extended 5 × 5 Sobels operator for estimating the maximum edges determined through the convolution of 24 pixels with eight templates to achieve 24 outputs corresponding to individual pixels for finding the maximum magnitude. It enhances the probability of connecting pixels in the vascular map with its closely located neighbourhood points in the fundus images. Then, the spatial information and kernel of the neighbourhood pixels are integrated through the Robust Semi-supervised Kernelized Fuzzy Local information C-Means Clustering (RSKFL-CMC) method to attain significant clustering process.FindingsThe results of the proposed DRDS architecture confirm the predominance in terms of accuracy, specificity and sensitivity. The proposed DRDS technique facilitates superior performance at an average of 99.64% accuracy, 76.84% sensitivity and 99.93% specificity.Research limitations/implicationsDRDS is proposed as a comfortable, pain-free and harmless diagnosis system using the merits of Dexcom G4 Plantinum sensors for estimating blood glucose level in diabetic patients. It uses the merits of RSKFL-CMC method to estimate the spatial information and kernel of the neighborhood pixels for attaining significant clustering process.Practical implicationsThe IoT architecture comprises of the application layer that inherits the DR application enabled Graphical User Interface (GUI) which is combined for processing of fundus images by using MATLAB applications. This layer aids the patients in storing the capture fundus images in the database for future diagnosis.Social implicationsThis proposed DRDS method plays a vital role in the detection of DR and categorization based on the intensity of disease into severe, moderate and mild grades. The proposed DRDS is responsible for preventing vision loss of diabetic Type-II patients by accurate and potential detection achieved through the utilization of IoT architecture.Originality/valueThe performance of the proposed scheme with the benchmarked approaches of the literature is implemented using MATLAB R2010a. The complete evaluations of the proposed scheme are conducted using HRF, REVIEW, STARE and DRIVE data sets with subjective quantification provided by the experts for the purpose of potential retinal blood vessel segmentation.

Entropy-like Divergence Based Kernel Fuzzy Clustering for Robust Image Segmentation

Gaussian kernel is defined by Euclidean distance and has been widely used in many fields. In the view of Euclidean distance is sensitive to outliers or noise and it is difficult to obtain satisfactory results for complex non-convex data. Entropy-like divergence is firstly induced by combining Jenson-Shannon/Bregman divergence with convex function, and its mercer kernel function called entropy-like divergence-based kernel is also constructed in this paper. Secondly, a new fuzzy weighted factor based on entropy-like divergence-based kernel is proposed by improving the existing trade-off weighting factor of kernel-based fuzzy local information C-means clustering (KWFLICM). In the end, a weighted fuzzy local information clustering based on entropy-like divergence-based kernel (EKWFLICM) is presented, which combines a new weighted fuzzy factor and entropy-like divergence-based kernel. Experiment results show that the proposed algorithm outperforms the segmentation performance of existing state-of-the-art fuzzy clustering-related algorithms for the image in presence of high noise.

Clustering based Multi-modality Medical Image Fusion

The unwanted data obtained through the medical image fusion is the main problem in biomedical applications, guided-image surgical and radiology. The Stationary Wavelet Transform (SWT) denoted the various advantages over conventional representation of imaging approach. In this research article we introduced innovative multi-modality fusion technique for medical image fusion based upon the SWT. In our approach it disintegrates of source images into approximation layers (coarse layer) and detail layers through the Stationary Wavelet Transform scheme, then applying of the Fuzzy Local Information C-means Clustering (FLICM) and Local contrast fusion approach to overcome the blurring effect, sensitiveness and conserve the quality evaluation in the distinguish layers. The demonstration shows that it preserves more detailed information in the source images and it enhances the more quality features and edge preserved of the final fused image obtained through the reconstruction procedure by recursive initial steps. The different methodologies with other techniques to evaluate performance such as mutual information, edge based similarity and blind image quality. This shows that in both the objective and subjective analysis our methodology results attained more supercilious performance.

Measurement of bleb height area and extent using anterior segment optical coherence tomography

To image trabecolectomy blebs using anterior segment optical coherence tomography AS-OCT and to measure the blebs morphological features such as bleb height area and extend. In this paper fuzzy local information C-means clustering is used to segment the bleb boundary. A batch of 25 AS-OCT images are used to assess the performance of the determined parameters to the clinical parameters, and 91.43% accuracy is obtained in the determined parameters result. The mean value of bleb height, area and extend are 0.2 mm, 1.618 mm2, 0.343 mm respectively. The result shows the potential applicability of the method for automated and objective mass screening for detection of bleb boundary.

Measurement of bleb height area and extent using anterior segment optical coherence tomography

To image trabecolectomy blebs using anterior segment optical coherence tomography AS-OCT and to measure the blebs morphological features such as bleb height area and extend. In this paper fuzzy local information C-means clustering is used to segment the bleb boundary. A batch of 25 AS-OCT images are used to assess the performance of the determined parameters to the clinical parameters, and 91.43% accuracy is obtained in the determined parameters result. The mean value of bleb height, area and extend are 0.2 mm, 1.618 mm2, 0.343 mm respectively. The result shows the potential applicability of the method for automated and objective mass screening for detection of bleb boundary.

Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method.

The explicit solution of the traditional ROF model in image denoising has the disadvantages of unstable results and requiring many iterations. To solve the problem, a new method, ROF model semi-implicit denoising, is proposed in this paper and applied to change detections of synthetic aperture radar (SAR) images. All remote sensing images used in this article have been calibrated by ENVI software. First, the ROF model semi-implicit denoising method is used to denoise the remote sensing images. Second, for the denoised images, difference images are obtained by the logarithmic ratio and mean ratio methods. The final difference image is obtained by principal component analysis fusion (PCA fusion) of the two difference images. Finally, the final difference image is clustered by fuzzy local information C-means clustering (FLICM) to obtain the change regions. The research results show that the proposed method has high detection accuracy and time operation efficiency.

Brain tissue segmentation using fuzzy clustering techniques.

Medical image segmentation is an essential step for most consequent image analysis tasks. Medical images can be segmented manually, but the accuracy of image segmentation using the automated segmentation algorithms is more when compared with the manual calculations. In this paper, an automated segmentation and classification of tissues are proposed for MR brain images. To classify MR brain image into three segments such as Grey Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF). Classification of brain into tissues helps to diagnose several diseases such as tumors, Alzheimer's disease, stroke, multiple sclerosis. An unsupervised clustering technique such as Fuzzy C-Means (FCM) algorithm has been widely used in segmenting the images. The spatial information is not fully utilized by the conventional clustering algorithm and hence it is not applicable for clustering a noisy image. We incorporate a method for image clustering called out as Reformulated Fuzzy Local information C-Means Clustering algorithm [RFLICM] which is a variant of traditional Clustering algorithm by considering both spatial and gray level information. In RFLICM, spatial distance is replaced by local coefficient of variation in a fuzzy manner. Experiments are conducted on brain images to validate the performance of the proposed technique in segmenting the medical images and the efficiency achieved in the presence of salt and pepper noise is 99.86%. Standard FCM, Fuzzy Local information C-means clustering algorithm [FLICM], Reformulated Fuzzy Local information C-means clustering algorithm [RFLICM] are compared to explore the accuracy of our proposed approach. Clustering results show that RFLICM segmentation method is appropriate for classifying tissues in brain MR image.

基于模糊局部信息C均值算法的网目调光谱反射率预测模型研究

基于模糊局部信息C均值算法的网目调光谱反射率预测模型研究

A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model

This paper presents a variation of the fuzzy local information c-means clustering (FLICM) algorithm that provides color texture image clustering. The proposed algorithm incorporates region-level spatial, spectral, and structural information in a novel fuzzy way. The new algorithm, called RFLICM, combines FLICM and region-level Markov random field model (RMRF) together to make use of large scale interactions between image patches instead of pixels. RFLICM can overcome the weakness of FLICM when dealing with textured images and at the same time enhances the clustering performance. The major characteristic of RFLICM is the use of a region-level fuzzy factor, aiming to guarantee texture homogeneity and preserve region boundaries. Experiments performed on synthetic and remote sensing images show that RFLICM is effective in providing accuracy to color texture images.