Spatial Fuzzy C-means Research Articles

Enhancing Intercultural Business English Communication Factors Evaluation System Using the Termite Life Cycle Optimization Algorithm and Dynamically Stabilized Recurrent Neural Network

In today's globalized business environment, effective intercultural communication in English is paramount for successful collaboration among professionals from diverse backgrounds. To enhance the accuracy of the evaluation system, enhancing intercultural business English communication factors evaluation system using the termite life cycle optimization algorithm and dynamically stabilized recurrent neural network (IBEC–DSRNN–TLCOA) is proposed in this manuscript. The input image is captured from mobile camera. Then the input images are preprocessed using intelligent weight decreasing firefly–particle filtering (IWDFPF) to remove noise and enhance the input images. Afterwards, the preprocessed image is fed to the entropy-founded spatial fuzzy C-means clustering approach for segmenting the image. Then the contrast, correlation, energy and homogeneousness features are extracted by using force-invariant improved feature extraction technique. The extracted features are given to dynamically stabilized recurrent neural network (DSRNN) to image target detection and English description generation. Termite life cycle optimization algorithm (TLCOA) is employed to enhance the weight parameters of DSRNN. The proposed IBEC–DSRNN–TLCOA method is implemented. The proposed IBEC–DSRNN–TLCOA method provides 32.53%, 31.86%, and 35.72% higher accuracy; 35.58%, 32.16%, and 37.72% higher F-measure when compared with the existing methods, such as exploration of intelligent translation with evaluation systems for business English (IBEC–RCNN), E-learning engagement with convolution neural networks on business education (IBEC–CNN), and deep neural network-based research on scoring business English oral training (IBEC–DNN), respectively.

Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images.

Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate segmentation is crucial for diagnosing brain injuries and neurodegenerative conditions. We introduce an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for 3D T1 MRI segmentation to three tissues, i.e. White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The esFCM employs a weighted least square algorithm utilizing the Structural Similarity Index (SSIM) for polynomial bias field correction. It also takes advantage of the information from the membership function of the last iteration to compute neighborhood impact. This strategic refinement enhances the algorithm's adaptability to complex image structures, effectively addressing challenges such as intensity irregularities and contributing to heightened segmentation accuracy. We compare the segmentation accuracy of esFCM against four variants of FCM, Gaussian Mixture Model (GMM) and FSL and ANTs algorithms using four various dataset, employing three measurement criteria. Comparative assessments underscore esFCM's superior performance, particularly in scenarios involving added noise and bias fields.The obtained results emphasize the significant potential of the proposed method in the segmentation of MRI images.

A CAD system design using iteratively reweighted fuzzy c-means and deep tree training for Alzheimer's disease diagnosis

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory loss and other cognitive abnormalities. In this research work, an MRI-based Computer-Aided Diagnosis (CAD) system is presented for clinical practice. While current algorithms used for detecting Alzheimer's disease perform inefficiently. Those algorithms are unable to detect minor changes in the disease due to discrepancies in feature extraction, segmentation, and classification methods. To overcome the issues in algorithm, a new iteratively reweighted adaptive spatial fuzzy c-means approach (IRW-ASFCM) is used to segment the brain tissue efficiently. Initially, the Magnetic resonance images (MRI) will pre-processed by using the Statistical parametric mapping (SPM) tool. After pre-processing, the segmentation is done using IRW-ASFCM to get effective tissue segments. Then, a 2-level selective Wavelet Kernel (2LS-WaveCNN ensemble) based CNN ensemble model will be used to extract the feature map from the Gray matter (GM), White matter (WM) and Cerebrospinal fluid (CSF) regions after segmenting the images. The Wave CNN networks built from hidden layers are trained using Deep Tree Training (DTT) for AD classification. The simulation results demonstrate that the suggested CAD system is superior to the state-of-the-art systems in terms of accuracy (99.54 %), sensitivity (99.26 %), and specificity (99.64 %) for multi-class problems.

A graph neural network framework for spatial geodemographic classification

Geodemographic classifications are exceptional tools for geographic analysis, business and policy-making, providing an overview of the socio-demographic structure of a region by creating an unsupervised, bottom-up classification of its areas based on a large set of variables. Classic approaches can require time-consuming preprocessing of input variables and are frequently a-spatial processes. In this study, we present a groundbreaking, systematic investigation of the use of graph neural networks for spatial geodemographic classification. Using Greater London as a case study, we compare a range of graph autoencoder designs with the official London Output Area Classification and baseline classifications developed using spatial fuzzy c-means. The results show that our framework based on a Node Attributes-focused Graph AutoEncoder (NAGAE) can perform similarly to classic approaches on class homogeneity metrics while providing higher spatial clustering. We conclude by discussing the current limitations of the proposed framework and its potential to develop into a new paradigm for creating a range of geodemographic classifications, from simple, local ones to complex classifications able to incorporate a range of spatial relationships into the process.

거래사례 기반 기계학습 공간군집화를 이용한 서울 오피스 시장 권역 분석

This study attempts to regionalize the Seoul office market by focusing on the investors’ perspectives. The study uses the transaction price data announced by the Ministry of Land, Infrastructure, and Transport and applies the Fuzzy C-Means algorithms, which are machine learning-based clustering techniques. The study found the following: First, as a result of comparing various algorithms, the Spatial Fuzzy C-Means algorithm performs the best clustering. Second, the three major office districts in Seoul form clusters differentiated from other areas. Third, the number of Dongs belonging to each district is smaller than the general consensus. The traditional business district (CBD) includes only parts of Jongno-Gu and Jung-Gu, the Yeouido business district (YBD) is composed of only Yeouido-Dong, and the Gangnam business district (GBD) comprises not many Dongs of Gangnam-Gu and Seocho-Gu. Fourth, by weakening the criteria, the clustering results in different spatial expansions by district. CBD has expanded to Seodaemun-Gu and Yongsan-Gu, whereas YBD has not expanded to Mapo-Gu or Yeongdeungpo. In particular, GBD expands to a considerable number of Dongs in Gangnam-Gu and Seocho-Gu. Fifth, the range of each district changes by period. When the office price increases rapidly, the range of each district is widened, and vice versa. These results show that, unlike the general perception of office districts, the CBD is expanding to Seodaemun-Gu and Yongsan-Gu. Moreover, Yeouido and nearby Mapo-Gu and Yeongdeungpo are not homogenous enough to be viewed as one district, and GBD is not linear but rectangular. Furthermore, Songpa-Gu does not form a spatially connected district with Seocho-Gu and Gangnam-Gu.

Image Formation Algorithms for Low-Cost Freehand Ultrasound Scanner Based on Ego-Motion Estimation and Unsupervised Clustering

This paper describes the application of unsupervised learning techniques to improve ego-motion estimation for a low-cost freehand ultrasound probe. Echo decorrelation measurements, which are used to estimate the lateral velocity of a scanning probe as it is passed over the skin, are found to be sensitive to varying tissue types and echogenicity in the imaged scene, and this can impact the geometric accuracy of the generated images. Here, we investigate algorithms to cluster the collated 1D echo data into regions of different echogenicity by applying a Gaussian mixture model (GMM), spatial fuzzy c-means (SFCM) or k-means clustering techniques, after which the decorrelation measurements can focus on the regions that yield the most accurate velocity estimates. A specially designed mechanical rig is used to provide the ground truth for the quantitative analysis of probe position estimation on phantom and in vivo data using different clustering techniques. It is concluded that the GMM is the most effective in classifying regions of echo data, leading to the reconstruction of the most geometrically correct 2D B-mode ultrasound image.

An optimal hybrid multiclass SVM for plant leaf disease detection using spatial Fuzzy C-Means model

In recent times, plant disease detection and diagnosing measures become a major concern in the agriculture field. Earlier identification of plant disease aids the farmers to take precautionary measures thereby preventing the spread of disease to other parts of plants. Based on the severity of the diseases, the plant may undergo an attack from mild to complete destruction. So, to avoid such destruction, plant diseases should be detected at the initial stage. Therefore, this paper proposes a novel hybrid random forest Multiclass SVM (HRF-MCSVM) design for plant foliar disease detection. To improve the computation accuracy, the image features are preprocessed and segmented using Spatial Fuzzy C-Means prior to the classification process. The Plant Village dataset used consists of a total of 54,303 healthy and diseased leaf images. Finally, the performance metrics like accuracy, F-measure, specificity, sensitivity, and recall value were evaluated to determine the effectiveness of the system. The proposed HRF-MCSVM method is compared with a few existing techniques to determine its efficiency.

Spatial Fuzzy C-Means Clustering Analysis of U.S. Presidential Election and COVID-19 Related Factors in the Rustbelt States in 2020

The rustbelt states play a key role in determining the vote turnout in the U.S. elections. The current study attempts to utilize the spatial fuzzy C-means method to analyze the U.S. presidential election in the rustbelt states in 2020. We intend to explore that the U.S. presidential election had related factors, including COVID-19-related factors, such as the mask-wearing percentage and the COVID-19 death tolls in each county of the rust belt states. Contrary to the related literature, the study uses education level, number of house units, unemployment rate, household income, COVID-19-related factors and the share of Republican’s votes in the presidential election. The results indicate that spatial generalized fuzzy C-means analysis has better clustering results than the C-means clustering method. Moreover, the COVID-19 death toll in each county did not affect the Republican’s vote share in the rustbelt states, while the mask-wearing behavior in some regions had a negative impact on the Republican’s vote share.

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation.

Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in human body. In research area, Fuzzy set is one of the most successful techniques that guarantees a robust classification. Spatial FCM (SFCM); one of the fuzzy c-means variants; considers spatial information to deal with the noisy images. To reduce this iterative algorithm’s execution time, a hard SIMD architecture has been planted named the Graphical Processing Unit (GPU). In this work, a great contribution has been done to diagnose, confront and implement three different parallel implementations on GPU. A parallel implementations’ extensive study of SFCM entitled PSFCM using 3 × 3 window is presented, and the experiments illustrate a significant decrease in terms of running time of this algorithm known by its high complexity. The experimental results indicate that the parallel version’s execution time is about 9.46 times faster than the sequential implementation on image segmentation. This gain in terms of speed-up is achieved on the Nvidia GeForce GT 740 m GPU.

Large-scale investigation of deep learning approaches for ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI

Respiratory diseases are leading causes of mortality and morbidity worldwide. Pulmonary imaging is an essential component of the diagnosis, treatment planning, monitoring, and treatment assessment of respiratory diseases. Insights into numerous pulmonary pathologies can be gleaned from functional lung MRI techniques. These include hyperpolarized gas ventilation MRI, which enables visualization and quantification of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. Here, we evaluate several 3D convolutional neural networks to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 759 helium-3 (3He) or xenon-129 (129Xe) volumetric scans and corresponding expert segmentations from 341 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined 3He and 129Xe MRI scans using a 3D nn-UNet outperformed other DL methods, achieving a mean ± SD Dice coefficient of 0.963 ± 0.018, average boundary Hausdorff distance of 1.505 ± 0.969 mm, Hausdorff 95th percentile of 5.754 ± 6.621 mm and relative error of 0.075 ± 0.039. Moreover, limited differences in performance were observed between 129Xe and 3He scans in the testing set. Combined training on 129Xe and 3He yielded statistically significant improvements over the conventional methods (p < 0.0001). In addition, we observed very strong correlation and agreement between DL and expert segmentations, with Pearson correlation of 0.99 (p < 0.0001) and Bland–Altman bias of − 0.8%. The DL approach evaluated provides accurate, robust and rapid segmentations of ventilated lung regions and successfully excludes non-lung regions such as the airways and artefacts. This approach is expected to eliminate the need for, or significantly reduce, subsequent time-consuming manual editing.

A novel framework for retinal vessel segmentation using optimal improved frangi filter and adaptive weighted spatial FCM

Medical attention has long been focused on diagnosing diseases through retinal vasculature. However, due to the image intensity inhomogeneity and retinal vessel thickness variability, segmenting the vessels from retinal images is still a tough matter. In this paper, we suggest an optimal improved Frangi-based multi-scale filter for enhancement. The parameters of the Frangi filter are optimised using a modified enhanced leader particle swarm optimization (MELPSO). The enhanced image is segmented using a novel adaptive weighted spatial fuzzy c-means (AWSFCM) clustering technique. The suggested approach is tested on three freely available databases. The results obtained are compared with state-of-the-art procedures. It is observed that the suggested approach outperforms other methods and may serve as an effective approach for retinal vessel segmentation.

Unsupervised change detection in SAR images based on generalized likelihood ratio test and a two-stage morphological filter

ABSTRACT Generalized likelihood ratio test (GLRT) is an efficient method to generate difference image (DI) for change detection (CD) using synthetic aperture radar (SAR) images. GLRT is usually applied with a fixed-size moving window to the neighbourhood regions in multitemporal SAR images. The fixed window may be however not optimal for all the pixels under test. To solve disadvantages of the GLRT method, an adaptive circular window is proposed in this work. While the adaptive square window and the best fixed square window achieve an average Kappa coefficient of 83.55% and 82.12%, respectively, the adaptive circular window improves the average Kappa coefficient by 83.65% in six datasets. As another difficulty, speckle noise reduces quality of DI in SAR change detection. Three steps are considered to minimize effects of the speckle noise: (1) a two-stage morphological filter is suggested to reduce the speckle noise; (2) to generate DI, the adaptive circular window for generalized likelihood ratio test (ACWGLRT) is proposed that reduces influence of the speckle noise while preserves the edge details of multitemporal images; and (3) spatial fuzzy c-means (SFCM) is used to reduce effects of the residual speckle-noise during DI classification. The experimental results show superior performance of the proposed change detection method with respect to several competitors. The proposed method has the best Kappa coefficient and percentage correct classification (PCC) in four datasets of Ottawa, San Francisco, Farmland C, and Inland water.

Climate zoning of asphalt pavement based on spatial interpolation and Fuzzy C-Means algorithm

ABSTRACT Natural conditions tend to affect the performance of the asphalt pavement. Climatic factors must be taken into account in the structural design of the asphalt pavement. Thus it is important to classify asphalt scientifically and reasonably based on the local climatic conditions. Based on 56-year meteorological data from 747 representative meteorological stations in China, temperature and precipitation were divided into eight meteorological indices. Three spatial interpolation methods are used for interpolation, namely Inverse Distance Weight, Ordinary Kriging and Collaborative Kriging considering Digital Elevation Model (DEM). After the comparison, the Collaborative Kriging considering DEM interpolation results of temperature and the Inverse Distance Weight interpolation results of precipitation were used to establish the standard climate zoning system in China. The classification of asphalt pavement is introduced on the basis of performance grading. According to the pavement high and low temperature design formula obtained by comparison, climatic suitability zoning of road asphalt is performed, and the classification table with performance grading of road asphalt in China is presented. The research results provide a theoretical basis for the selection of road asphalt and provide guidance for scientific and reasonable design of asphalt pavement.

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.

Liver Segmentation from CT Volumes Based on Spatial Fuzzy C-Means and Graph Cuts

Liver Segmentation from CT Volumes Based on Spatial Fuzzy C-Means and Graph Cuts

Wasserstein GAN-gradient penalty with deep transfer learning based alzheimer disease classification on 3D MRI scans

There has been growing interest in using neuroimaging data, such as MRI scans, for the detection of Alzheimer's Disease (AD). Computer vision and deep learning models have shown promise in developing effective Computer-Aided Diagnosis (CAD) models for AD detection and classification. However, many existing models struggle due to their reliance on large training datasets and effective hyper parameter tuning strategies. To address these issues, transfer learning is often used to adjust the final fully connected layers of pre-trained DL models for use with smaller datasets. This paper proposes a new AD classification model based on a combination of Wasserstein GAN-Gradient Penalty (WGANGP) and Deep Transfer Learning (DTL) techniques, aimed at achieving accurate identification and classification of AD on 3D MRI scans. The WGANGP technique is used to increase the size of the dataset, and the model utilizes image enhancement and 3D Spatial Fuzzy C-means (3DS-FCM) techniques for image segmentation. Additionally, feature extraction is performed using the Ant Lion Optimizer (ALO) with the Inception v3 model, while the Deep Belief Network (DBN) model is employed for AD classification. The experimental validation of the WGANGP-DTL model is conducted using a benchmark 3D MRI dataset, and the results show that the proposed model outperforms recent approaches in several aspects.

On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI

Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (sFCM). They represent well-established pattern recognition techniques that are still widely used in clinical research. Starting from the basic versions of these segmentation approaches, during our analysis, we identified the shortcomings of each of them, proposing improved versions, as well as developing ad hoc pre- and post-processing steps. The obtained experimental results, in terms of area-based—namely, Dice Index (DI), Jaccard Index (JI), Sensitivity, Specificity, False Positive Ratio (FPR), False Negative Ratio (FNR)—and distance-based metrics—Mean Absolute Distance (MAD), Maximum Distance (MaxD), Hausdorff Distance (HD)—encourage the use of unsupervised machine learning techniques in medical image segmentation. In particular, fuzzy clustering approaches (namely, FCM and sFCM) achieved the best performance. In fact, for area-based metrics, they obtained DI = 78.23% ± 6.50 (sFCM), JI = 65.90% ± 8.14 (sFCM), sensitivity = 77.84% ± 8.72 (FCM), specificity = 87.10% ± 8.24 (sFCM), FPR = 0.14 ± 0.12 (sFCM), and FNR = 0.22 ± 0.09 (sFCM). Concerning distance-based metrics, they obtained MAD = 1.37 ± 0.90 (sFCM), MaxD = 4.04 ± 2.87 (sFCM), and HD = 2.21 ± 0.43 (FCM). These experimental findings suggest that further research would be useful for advanced fuzzy logic techniques specifically tailored to medical image segmentation.

Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema

A Computer-Aided Diagnosis (CAD) system to assist a radiologist for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been developed. The lung tissues are segmented from the chest CT slices using Spatial Fuzzy C-Means (SFCM) clustering algorithm and the Regions of Interest (ROIs) are extracted using pixel-based segmentation. The ROIs considered for this work are pulmonary emphysematous lesions namely, centrilobular emphysema, paraseptal emphysema and sub-pleural bullae. The extracted ROIs are then validated and labelled by an expert radiologist. From each ROI, features with respect to shape, texture and run-length are extracted. A competitive coevolution model is proposed for Feature Selection (FS). The model makes use of two bio-inspired algorithms namely, Spider Monkey Optimization (SMO) algorithm and Paddy Field Algorithm (PFA) as its building blocks. FS is performed as a wrapper approach, using the bio-inspired algorithms namely, SMO and PFA with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function. Ten-fold cross validation technique is used in training the SVM classifier using the selected features. The model is tested using two datasets: Real-time emphysema dataset and CT emphysema database (CTED) dataset. The accuracy, precision, recall and specificity obtained for both the datasets are (81.95%, 93.74%), (72.92%, 90.61%), (72.92%, 90.61%), (86.46%, 95.3%), which are better compared to the performance of SMO and PFA algorithms applied individually for FS and the CAD system without performing FS.

An Automatic Detection of Liver Tumor from CT Abdominal Images—A Comparative Approach

The liver is a vital organ in human body. Liver performs an important function including metabolism, digestion, and detoxification. Liver is a significant organ in an abdomen, and is connected to the nearby organ such as spleen, pancreas, gallbladder, abdomen, and gut through blood vessels. Specific approaches such as image gradient and region growing are not quite reliable for the segmentation of the liver tumor. A level-set approach is evaluated in this paper compared with the active contour approach of segmentation of the liver imaging from the image of the CT abdomen and Unified level set method, spatial Fuzzy C-means method for segmenting tumor from segmented liver images is appraised. The proposed approach is implemented by using the 3DIRCADB dataset available to the public as well as non-public datasets taken from Arthi Hospital, Chennai and Tirunelveli scanning centre. For validating the system based on the diverse quantitative measures, including space overlap, coefficient of similarity, Jaccard indices, using ground truth images, which are available in the public data set 3DIRCADB and the expert segmentation results which are manually identified by the clinical partner for nonpublic datasets. The analysis of the algorithm shows the better results for segmenting liver using level set system and spatial segmentation of Fuzzy C means of the tumor segmentation.

Soil and Climate Characterization to Define Environments for Summer Crops in Senegal

Investigating soil and climate variability is critical to defining environments for field crops, understanding yield-limiting factors, and contributing to the sustainability and resilience of agro-ecosystems. Following this rationale, the aim of this study was to develop a soil–climate characterization to describe environmental constraints in the Senegal summer-crops region. For the soil database, 825 soil samples were collected characterizing pH, electrical conductivity (EC), phosphorus (P), potassium (K), cation exchange capacity (CEC), and total carbon (C) and nitrogen (N). For the climate, monthly temperature, precipitation, and evapotranspiration layers were retrieved from WorldClim 2.1, CHIRPS and TERRACLIMATE. The same analysis was applied individually to both databases. Briefly, a principal component analysis (PCA) was executed to summarize the spatial variability. The outcomes from the PCA were subjected to a spatial fuzzy c-means algorithm, delineating five soil and three climate homogeneous areas, accounting for 73% of the soil and 88% of the climate variation. To our knowledge, no previous studies were done with large soil databases since availability field data is often limited. The use of soil and climate data allowed the characterization of different areas and their main drivers. The use of this classification will assist in developing strategic planning for future land use and capability classifications.