Spatial Fuzzy Clustering Research Articles

A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image Segmentation

In order to reduce the computational complexity of multi-objective evolutionary optimization-based clustering algorithms, a Kriging-assisted reference vector guided multi-objective robust spatial fuzzy clustering algorithm (KRV-MRSFC) is proposed and then successfully applied to image segmentation. We first construct objective functions with noise robust local spatial information derived from the image to improve the robustness to noise and then use the Kriging model to approximate each objective function to decrease the computational cost. Meanwhile, in order to improve the approximation accuracy of the Kriging model, an angle-penalized distance-based expected improvement sampling criterion is presented in the KRV-MRSFC, which can select individuals with better exploitation and exploration to update the Kriging model. In addition, KRV-MRSFC adopts a clustering validity index with noise robust local image spatial information to select the optimal solution from the final non-dominated solution set to perform image segmentation. The experiments performed on Berkeley and real magnetic resonance images indicate that the proposed method not only achieves satisfactory segmentation performance on noisy images but also requires a low time cost.

Iterative spatial fuzzy clustering for 3D brain magnetic resonance image supervoxel segmentation

BackgroundAlthough supervoxel segmentation methods have been employed for brain Magnetic Resonance Image (MRI) processing and analysis, due to the specific features of the brain, including complex-shaped internal structures and partial volume effect, their performance remains unsatisfactory. New methodsTo address these issues, this paper presents a novel iterative spatial fuzzy clustering (ISFC) algorithm to generate 3D supervoxels for brain MRI volume based on prior knowledge. This work makes use of the common topology among the human brains to obtain a set of seed templates from a population-based brain template MRI image. After selecting the number of supervoxels, the corresponding seed template is projected onto the considered individual brain for generating reliable seeds. Then, to deal with the influence of partial volume effect, an efficient iterative spatial fuzzy clustering algorithm is proposed to allocate voxels to the seeds and to generate the supervoxels for the overall brain MRI volume. ResultsThe performance of the proposed algorithm is evaluated on two widely used public brain MRI datasets and compared with three other up-to-date methods. ConclusionsThe proposed algorithm can be utilized for several brain MRI processing and analysis, including tissue segmentation, tumor detection and segmentation, functional parcellation and registration.

Nonsubsampled contourlet domain visible and infrared image fusion framework for fire detection using pulse coupled neural network and spatial fuzzy clustering

The rapidly spreading forest fire is always uncontrollable and unpredictable and also has a disastrous effect on the environment as well as human individuals. This is also able to wipe out large acres of forest as well as the agricultural lands. Therefore, in this paper, a fire detection approach is presented in the nonsubsampled contourlet (NSCT) domain by extracting the fused fire regions of visible and infrared (IR) images using spatial fuzzy C-means clustering (SpFCM). Firstly, the NSCT is applied to decompose the source visible and IR images into one low and several high-frequency components. Low-frequency NSCT component is fused using a pulse coupled neural network (PCNN) motivated by the sum-modified Laplacian to retain the maximum information available in both the source images. Local log Gabor energy based fusion rule is employed to fuse the high-frequency NSCT components that are able to preserve the maximum detail information. Later, the fused image is reconstructed by applying the inverse NSCT. Finally, all the fire pixels are identified in the fused images and segmented using the fuzzy clustering approach having spatial information also. Furthermore, several experiments are conducted to evaluate the fire detection ability of the proposed framework in terms of visual appearance as well as several performance evaluation parameters. Experimental results show the superiority of the proposed approach over the other existing fusion approaches by improving all the performance parameters.

Brain Image Segmentation Based on Fuzzy Clustering

The segmentation performance is topic to suitable initialization and best configuration of supervisory parameters. In medical image segmentation, the segmentation is very important when the diagnosing becomes very hard in medical images which are not properly illuminated.
 This paper proposes segmentation of brain tumour image of MRI images based on spatial fuzzy clustering and level set algorithm. After performance evaluation of the proposed algorithm was carried on brain tumour images, the results showed confirm its effectiveness for medical image segmentation, where the brain tumour is detected properly.

Unpaved road detection based on spatial fuzzy clustering algorithm

Vision-based unpaved road detection is a challenging task due to the complex nature scene. In this paper, a novel algorithm is proposed to improve the accuracy and robustness of unpaved road detection and boundary extraction with low computational costs. The novelties of this paper are as follows: (1) We use a normal distribution with infrared images to detect the vanishing line, and a trapezoid prediction model is proposed according to the road shape features. (2) Road recognition based on connected regions is implemented by an improved support vector machine (SVM) classifier with a normalized class feature vector. According to the recognition results, the road probability confidence map is obtained. (3) With the help of fusing continuous information with the trapezoidal forecasting model and the probability from the confidence map, we present a road probability recognition method based on the trapezoidal forecasting model and spatial fuzzy clustering. Furthermore, the histogram backprojection model is used to solve interference problems caused by shadows on the road. It takes approximately 0.012~0.014 s to process one frame of an image for the road recognition, and the accuracy rate can reach 93.2%. The experimental results show that the algorithm can achieve better performance than some state-of-the-art methods in terms of detection accuracy and speed.

A Fuzzy clustering based MRI brain image segmentation using back propagation neural networks

The paper focuses on how a brain image is being segmented to diagnose the brain tumor by using spatial fuzzy clustering algorithm. The segmentation process may cause error while diagonizing MR images due to the artifacts and noises exist in it. This may leads to misclassify the normal tissue as abnormal tissue. The proposed method is to segment normal tissues such as White Matter, Gray Matter, Cerebrospinal fluid and abnormal tissue like tumor part from MR images automatically. It comprises of pre-processing step, using wrapping based curvelet transform to remove noise and modified spatial fuzzy C-Means algorithm (Liu et al. in Tsinghua Sci Technol 19(6):578–595, 2014; Kwak and Choi in IEEE Trans Neural Netw 13(1):143–159, 2014) segments normal tissues by considering spatial information. The neighboring pixels are highly correlated and construct initial membership matrix randomly. The process is also intended to segment the tumor cells as well as the removal of background noises for smoothening the region, results in presenting segmented tissues and parameter evaluation to produce the algorithm efficiency.

Glioma Segmentation Using a Novel Unified Algorithm in Multimodal MRI Images

To achieve the better segmentation performance, we propose a unified algorithm for automatic glioma segmentation. In this paper, we first use spatial fuzzy c-mean clustering to estimate region-of-interest in multimodal MRI images, and then extract some seed points from there for region growing based on a new notion “affinity”. In the end, we design a two-step strategy to refine the glioma border with region merging and improved distance regularization level set method. In BRATS 2015 database, we evaluate the accuracy and robustness of our method with performance scores, including dice, positive predictive value (PPV), and sensitivity metrics, as well as Hausdorff and Euclidean distance (HD&ED). The high metric values (dice = 0.86, PPV = 0.90, and sensitivity = 0.84) and small distance errors (HD = 14.39 mm and ED = 3.31 mm) indicate a remarkable accuracy. Also, we observe the ranking is No.1 in terms of dice and PPV, comparing with the state-of-the-art methods. In addition, the robustness is also at a high-level due to the refinement structure. And Spearman’s rank coefficient test verities a significant correlation between the high-grade gliomas and low-grade gliomas. Overall, the proposed method is effective in segmenting gliomas in multimodal images or flair images, and has the potential in routine examinations of gliomas in daily clinical practice.

Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

ABSTRACTCoastline extraction from synthetic aperture radar (SAR) data is difficult because of the presence of speckle noise and strong signal returns from the wind-roughened and wave-modulated sea surface. High resolution and weather change independent of SAR data lead to better monitoring of coastal sea. Therefore, SAR coastline extraction has taken up much interest. The active contour method is an efficient algorithm for the edge detection task; however, applying this method to high-resolution images is time-consuming. The current article presents an efficient approach to extracting coastlines from high-resolution SAR images. First, fuzzy clustering with spatial constraints is applied to the input SAR image. This clustering method is robust for noise and shows good performance with noisy images. Next, binarization is carried out using Otsu’s method on the fuzzification results. Third, morphological filters are used on the binary image to eliminate spurious segments after binarization. To extract the coastline, an active contour level set method is used on the initial contours and is applied to the input SAR image to refine the segmentation. Because the proposed approach is based on an active contour model, it does not require preprocessing for SAR speckle reduction. Another advantage of the proposed method is the ability to extract the coastline at full resolution of the input SAR image without degrading the resolution. The proposed approach does not require manual initialization for the level set method and the proposed initialization speeds up the level set evolution. Experimental results on low- and high-resolution SAR images showed good performance for coastline extraction. A criterion based on neighbourhood pixels for the coastline is proposed for the quantitative expression of the accuracy of the method.

Adaptive chemical reaction based spatial fuzzy clustering for level set segmentation of medical images

Abstract The chemical reaction optimization (CRO), inspired from the interactions of molecules during chemical reactions in reaching a low energy stable state, searches for optimal solution through simulated reactions involving the on-wall ineffective collisions, decomposition, inter-molecular ineffective collision and synthesis. This paper attempts to obtain the global best centroids for spatial fuzzy clustering (SFC) using adaptive CRO (ACRO) with a view to facilitate the level set segmentation for medical images. The approach helps to analyze tumors or unhealthy region in various medical images. The results of medical images of brain, liver, abdomen and eye images are presented to demonstrate the performance.

Fully automated, level set-based segmentation for knee MRIs using an adaptive force function and template: data from the osteoarthritis initiative.

BackgroundThis study focuses on osteoarthritis (OA), which affects millions of adults and occurs in knee cartilage. Diagnosis of OA requires accurate segmentation of cartilage structures. Existing approaches to cartilage segmentation of knee imaging suffer from either lack of fully automatic algorithm, sub-par segmentation accuracy, or failure to consider all three cartilage tissues.MethodsWe propose a novel segmentation algorithm for knee cartilages with level set-based segmentation method and novel template data. We used 20 normal subjects from osteoarthritis initiative database to construct new template data. We adopt spatial fuzzy C-mean clustering for automatic initialization of contours. Force function of our algorithm is modified to improve segmentation performance.ResultsThe proposed algorithm resulted in dice similarity coefficients (DSCs) of 87.1, 84.8 and 81.7 % for the femoral, patellar, and tibial cartilage, respectively from 10 subjects. The DSC results showed improvements of 8.8, 4.3 and 3.5 % for the femoral, patellar, and tibial cartilage respectively compared to existing approaches. Our algorithm could be applied to all three cartilage structures unlike existing approaches that considered only two cartilage tissues.ConclusionsOur study proposes a novel fully automated segmentation algorithm adapted for three types of knee cartilage tissues. We leverage state-of-the-art level set approach with newly constructed knee template. The experimental results show that the proposed method improves the performance by an average of 5 % over existing methods.

Spatial fuzzy clustering approach to characterize flood risk in urban storm water drainage systems

Increasing environmental stress of drainage systems leads to frequent occurrence of urban flooding, which generates significant adverse economic, social, and environmental impacts. In this study, spatial fuzzy clustering approach (SFCA) is developed to estimate possible flood risks in storm water drainage systems and address complexity and uncertainties of flood risk assessment. The proposed approach is capable of gaining insights into system behavior by exploring spatial patterns of flood risk. The corresponding algorithm is utilized to divide a drainage system into various clusters to reflect flood risk levels along the network space. Application to a sample drainage system demonstrates that it provides an appropriate technique to determine spatial distribution of flood risks. Results reveal that areas with high, moderate, and low risk identified by SFCA correspond to a certain degree of environmental stress. The findings of this study can serve as a preliminary basis to guide managers in their evaluation of flood risks in various drainage management scenarios.

A Spatial Fuzzy Clustering Algorithm With Kernel Metric Based on Immune Clone for SAR Image Segmentation

The fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, FCM exhibits poor robustness to noise, often leading to unsatisfactory segmentations on noisy images. Additionally, the FCM algorithm is sensitive to the choice of initial cluster centers. In order to solve these problems, this paper proposes clone kernel spatial FCM (CKS_FCM), which improves segmentation performance in several ways. First, in CKS_FCM, an immune clone algorithm is used to generate the initial cluster centers, which helps prevent the algorithm from converging on local optima. Second, CKS_FCM improves the robustness to noise by incorporating spatial information into the objective function of FCM. Third, CKS_FCM uses a non-Euclidean distance based on a kernels metric, instead of the Euclidean distance conventionally used in FCM, to enhance the segmentation accuracy (SA). We present experimental results on both real and synthetic SAR images, which suggest that the proposed method can generate higher accuracy, and obtain more robustness to noise, as compared against six state-of-the-art methods from the literatures.

A Comparative Study on Clustering Algorithms using Image Data

of image called Segmentation .It is an important concept to viewing and analyzing different type's images and solving a wide range of problems in image. Clustering algorithm and technique for classifying usage image data and the process of analyze image data from dissimilar perception and abbreviation it into valuable information, this information can be use to increase proceeds, cuts costs, or Time complexity. There is different type of algorithms for image data and clustering such as (FCM) fuzzy c-means clustering algorithms, SFCM (Spatial fuzzy c-means clustering), K- Means, and PSOFCM (particle swarm optimization incorporative fuzzy c-means clustering) .The selection between the predictive classifier is extremely important. Fuzzy algorithms based on initial cluster selection without noise data. PSOFCM and SFCM approaches shows better segmentation results can be obtained in noise. PSOFCM and SFCM approaches shows how better image segmentation of results can be obtained. Image clustering and its applications are used in human image i.e. Medical image segmentation used for detection of Brain images, tumor and more. The result obtained through Particle swarm optimization (PSO), yields better detected image and time complexity compared to FCM and SFCM.

Image Classification using MRI Images in Brain Tumor

This article presents the classification of brain images to sense the stages using unverified classifier and abnormal detection through a spatial Fuzzy clustering algorithm.

Classification of benign and malignant breast tumors based on hybrid level set segmentation

Computer-aided Diagnosis (CADx) technology can substantially aid in early detection and diagnosis of breast cancers. However, the overall performance of a CADx system is tied, to a large extent, to the accuracy with which the tumors can be segmented in a mammogram. This implies that the segmentation of mammograms is a critical step in the diagnosis of benign and malignant tumors. In this paper, we develop an enhanced mammography CADx system with an emphasis on the segmentation step. In particular, we present two hybrid algorithms based upon region-based, contour-based and clustering segmentation techniques to recognize benign and malignant breast tumors. In the first algorithm, in order to obtain the most accurate final segmented tumor, the initial segmented image, that is required for the level set, is provided by one of spatial fuzzy clustering (SFC), improved region growing (RG), or cellular neural network (CNN). In the second algorithm, all of the parameters which control the level set are obtained from a dynamic training procedure by the combination of both genetic algorithms (GA) and artificial neural network (ANN) or memetic algorithm (MA) and ANN. After segmenting tumors using one of the hybrid proposed methods, intensity, shape and texture features are extracted from tumors, and the appropriate features are then selected by another GA algorithm. Finally, to classify tumors as benign or malignant, different classifiers such as ANN, random forest, naïve Bayes, support vector machine (SVM), and K-nearest neighbor (KNN) are used. Experimental results confirm the efficiency of the proposed methods in terms of sensitivity, specificity, accuracy and area under ROC curve (AUC) for the classification of breast tumors. It was concluded that RG and GA in adaptive RG-LS method produce more accurate primary boundary of tumors and appropriate parameters for the level set technique in segmentation and subsequently in classification.

Modeling European agri-environmental measure of spatial impact in the region of Sardinia, Italy, through fuzzy clustering means

The aim of this paper is to demonstrate how a spatial fuzzy clustering mean expands the knowledge of the European agri-environmental initiative impact, named Measure 214, in the Sardinia Region. While sketching out the geographic area covered by the measure for analysis and investigation using fcm is a fruitful approach, their integration with social and economic factors is an essential step in understanding agricultural growth and how it is influenced by environmental policy. This integrated approach shows how agri-environmental measures tend to develop in the region and, geographically, describes the spatial effects. Fuzzy clustering analysis demonstrates how decisions, whether they are related to the pursuit of policies moving towards the agri-environmental initiatives of organic farming and sustainable agriculture, or whether they concern ways of financing the measure’s activities, belong to the sphere of information, able to influence the new phase of agri-environmental financing and to keep it going. The spatial expansion of the measure all over the Region can help identify where the measure has taken root and in which directions it should be steered to achieve sustainable agri-environmental development in the area. Furthermore, the fuzzy cluster analysis highlights the relevance of the results, showing the policy direction that clusters should take in order to improve the measure’s effectiveness.

A multiobjective spatial fuzzy clustering algorithm for image segmentation

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, fuzzy c-means, two fuzzy c-means clustering algorithms with spatial information and a multiobjective variable string length genetic fuzzy clustering algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation.

Fully Automated Complementary DNA Microarray Segmentation using a Novel Fuzzy-based Algorithm.

DNA microarray is a powerful approach to study simultaneously, the expression of 1000 of genes in a single experiment. The average value of the fluorescent intensity could be calculated in a microarray experiment. The calculated intensity values are very close in amount to the levels of expression of a particular gene. However, determining the appropriate position of every spot in microarray images is a main challenge, which leads to the accurate classification of normal and abnormal (cancer) cells. In this paper, first a preprocessing approach is performed to eliminate the noise and artifacts available in microarray cells using the nonlinear anisotropic diffusion filtering method. Then, the coordinate center of each spot is positioned utilizing the mathematical morphology operations. Finally, the position of each spot is exactly determined through applying a novel hybrid model based on the principle component analysis and the spatial fuzzy c-means clustering (SFCM) algorithm. Using a Gaussian kernel in SFCM algorithm will lead to improving the quality in complementary DNA microarray segmentation. The performance of the proposed algorithm has been evaluated on the real microarray images, which is available in Stanford Microarray Databases. Results illustrate that the accuracy of microarray cells segmentation in the proposed algorithm reaches to 100% and 98% for noiseless/noisy cells, respectively.

Classification of high resolution satellite images using spatial constraints-based fuzzy clustering

A spatial constraints-based fuzzy clustering technique is introduced in the paper and the target application is classification of high resolution multispectral satellite images. This fuzzy-C-means (FCM) technique enhances the classification results with the help of a weighted membership function (Wmf). Initially, spatial fuzzy clustering (FC) is used to segment the targeted vegetation areas with the surrounding low vegetation areas, which include the information of spatial constraints (SCs). The performance of the FCM image segmentation is subject to appropriate initialization of Wmf and SC. It is able to evolve directly from the initial segmentation by spatial fuzzy clustering. The controlling parameters in fuzziness of the FCM approach, Wmf and SC, help to estimate the segmented road results, then the Stentiford thinning algorithm is used to estimate the road network from the classified results. Such improvements facilitate FCM method manipulation and lead to segmentation that is more robust. The results confirm its effectiveness for satellite image classification, which extracts useful information in suburban and urban areas. The proposed approach, spatial constraint-based fuzzy clustering with a weighted membership function (SCFCWmf), has been used to extract the information of healthy trees with vegetation and shadows showing elevated features in satellite images. The performance values of quality assessment parameters show a good degree of accuracy for segmented roads using the proposed hybrid SCFCWmf-MO (morphological operations) approach which also occluded nonroad parts.

A robust MR-based attenuation map generation in short-TE MR images of the head employing hybrid spatial fuzzy C-means clustering and intensity inhomogeneity correction.

Deriving an accurate attenuation correction map (μ-map) from magnetic resonance (MR) volumes has become an important problem in hybrid PET/MR imaging. Recently, short echo-time (STE) MR imaging technique incorporating fuzzy C-means (FCM) tissue classification and 2-point Dixon image acquisition has been introduced as a feasible approach for segmentation of the bone from air and soft tissue. However, this method imposes additional imaging and the performance of the standard FCM algorithm, suffering from the lack of spatial information, becomes impaired in the presence of inherent noise and intensity inhomogeneity. Here, we exploit a spatial fuzzy C-means (SFCM) segmentation algorithm in combination with a robust intensity inhomogeneity correction method on single STE-MR images, to differentiate various tissue classes. MR images of five subjects were acquired on a clinical 1.5T MRI System, MAGNETOM Avanto, using a FLASH 3D pulse sequence with TE=1.1ms, TR=12ms, flip angle=18°, voxel size=1.2×1.2×2mm3. The proposed segmentation approach consists of four main steps: (1) Intensity-inhomogeneity correction, to separate air and bone in the regions with high inhomogeneity, like the nasal areas; (2) applying SFCM to segment the images into four clusters including air, a part of soft tissue and bone, and two other soft tissue classes. Upon this step, the air cluster would be accurately separated; (3) employing shape factor analysis to remove the eyes with close signal intensity to that of bone; and (4) μ-map generation by downsampling and assigning attenuation coefficients to the corresponding segmented tissues. Quantitative evaluation indicated sensitivity of over 95% for air and soft tissue and about 81% in the bony region, and specificity of over 95% for all tissue classes. The proposed STE-MR imaging in combination with the segmentation technique could be potentially exploited as an efficient approach to generate MR-based attenuation correction maps in clinical PET/MR applications, where bone and air coexist.