Conventional Fuzzy C-means Algorithm Research Articles

Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine

Fuzzy C-Means (FCM) plays a major role in brain tissue segmentation. The proposed method aims to implements rapid brain tissue segmentation from MRI human head scans using FCM in CPU and GPU. This method is known as FCM-GENIUS. This paper presents three novel steps to enrich the performance of conventional FCM algorithm in CPU. There are region of interest (ROI) selection, knowledge based initialization and knowledge based optimization. The ROI selection is a preprocessing step contains brain extraction and bounding box processes. An automatic knowledge based initialization to FCM algorithm using histogram smoothing for centroids selection from middle slice of the given MRI brain volume. Optimization helps to improve the computation speed up of FCM algorithm using MRI slice adjacency property. The materials used for the proposed work are gathering from internet brain segmentation repository (IBSR). The accuracy of segmentation also compared with traditional and existing methods. The proposed method yield equal segmentation accuracy compared with existing methods but reduces the segmentation time considerably up to seven times and average number of iterations up to three times. In addition, parallel FCM implements in GPU machine and the performance was compared with the conventional FCM in CPU. The single instruction multiple data (SIMD) model was used with the hybrid CPU–GPU implementation in the GPU machine to accelerate the medical image segmentation.

A fuzzy c-means algorithm guided by attribute correlations and its application in the big data analysis of tunnel boring machine

Tunnel boring machine (TBM) is a complex engineering system used for tunnel construction, and its design is mainly based on knowledge from previous projects. With the development of measurement techniques, massive operation data have been recorded and partitioning these data can provide useful references to the designers and help in the design of TBM. In this paper, a fuzzy c-means algorithm guided by attribute correlations, named attribute correlation-guided fuzzy c-means algorithm (ACFCM), is proposed to accomplish this work. The proposed algorithm is based on fuzzy c-means algorithm (FCM) and involves a new objective function in which the attribute correlation is described by the linear model. A synthetic dataset is used to evaluate the performance of the ACFCM algorithm, which demonstrates its higher effectiveness and advantages compared with conventional FCM. The ACFCM algorithm is applied to cluster the TBM operation data from a tunnel in China, and the load and penetration rate of the TBM are predicted based on the clustering results. The results indicate that the ACFCM algorithm can not only provide competitive clustering results but also significantly increase prediction accuracy. This work also addresses the applicability and potential of data clustering in the design and analysis of other complex engineering systems similar to TBMs.

A Novel Adaptive Fuzzy Local Information $C$ -Means Clustering Algorithm for Remotely Sensed Imagery Classification

This paper presents a novel adaptive fuzzy local information c-means (ADFLICM) clustering approach for remotely sensed imagery classification by incorporating the local spatial and gray level information constraints. The ADFLICM approach can enhance the conventional fuzzy c-means algorithm by producing homogeneous segmentation and reducing the edge blurring artifact simultaneously. The major contribution of ADFLICM is use of the new fuzzy local similarity measure based on pixel spatial attraction model, which adaptively determines the weighting factors for neighboring pixel effects without any experimentally set parameters. The weighting factor for each neighborhood is fully adaptive to the image content, and the balance between insensitiveness to noise and reduction of edge blurring artifact to preserve image details is automatically achieved by using the new fuzzy local similarity measure. Four different types of images were used in the experiments to examine the performance of ADFLICM. The experimental results indicate that ADFLICM produces greater accuracy than the other four methods and hence provides an effective clustering algorithm for classification of remotely sensed imagery.

Fast and accurate fuzzy C‐means algorithm for MR brain image segmentation

ABSTRACTFuzzy theory based intelligent techniques are widely preferred for medical applications because of high accuracy. Among the fuzzy based techniques, Fuzzy C‐Means (FCM) algorithm is popular than the other approaches due to the availability of expert knowledge. But, one of the hidden facts is that the computational complexity of the FCM algorithm is significantly high. Since medical applications need to be time effective, suitable modifications must be made in this algorithm for practical feasibility. In this study, necessary changes are included in the FCM approach to make the approach time effective without compromising the segmentation efficiency. An additional data reduction approach is performed in the conventional FCM to minimize the computational complexity and the convergence rate. A comparative analysis with the conventional FCM algorithm and the proposed Fast and Accurate FCM (FAFCM) is also given to show the superior nature of the proposed approach. These techniques are analyzed in terms of segmentation efficiency and convergence rate. Experimental results show promising results for the proposed approach. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 188–195, 2016

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

The fuzzy C-means (FCM) algorithm has got significant importance due to its unsupervised form of learning and more tolerant to variations and noise as compared to other methods in medical image segmentation. In this paper, we propose a conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm. This is achieved through the incorporation of conditioning effects imposed by an auxiliary (conditional) variable corresponding to each pixel, which describes a level of involvement of the pixel in the constructed clusters, and spatial information into the membership functions. The problem of sensitivity to noise and intensity inhomogeneity in magnetic resonance imaging (MRI) data is effectively reduced by incorporating local and global spatial information into a weighted membership function. The experimental results on four volumes of simulated and one volume of real-patient MRI brain images, each one having 51 images, show that the csFCM algorithm has superior performance in terms of qualitative and quantitative studies such as, cluster validity functions, segmentation accuracy, tissue segmentation accuracy and receiver operating characteristic (ROC) curve on the image segmentation results than the k-means, FCM and some other recently proposed FCM-based algorithms.

Performance Improved Modified Fuzzy C-Means Algorithm for Image Segmentation Applications

Fuzzy C-Means (FCM) algorithm is one of the commonly preferred fuzzy algorithms for image segmentation applications. Even though FCM algorithm is sufficiently accurate, it suffers from the computational complexity problem which prevents the usage of FCM in real-time applications. In this work, this convergence problem is tackled through the proposed Modified FCM (MFCM) algorithm. In this algorithm, several clusters among the input data are formed based on similarity measures and one representative data from each cluster is used for FCM algorithm. Hence, this methodology minimizes the convergence time period requirement of the conventional FCM algorithm to higher extent. This proposed approach is experimented on Magnetic Resonance (MR) brain tumor images. Experimental results suggest promising results for the MFCM algorithm in terms of the performance measures.

Automatic histogram-based fuzzy C-means clustering for remote sensing imagery

Fuzzy C-means (FCM) clustering has been widely used in analyzing and understanding remote sensing images. However, the conventional FCM algorithm is sensitive to initialization, and it requires estimations from expert users to determine the number of clusters. To overcome the limitations of the FCM algorithm, an automatic histogram-based fuzzy C-means (AHFCM) algorithm is presented in this paper. Our proposed algorithm has two primary steps: 1 – clustering each band of a multispectral image by calculating the slope for each point of the histogram, in two directions, and executing the FCM clustering algorithm based on specific rules, and 2 – automatic fusion of labeled images is used to initialize and determine the number of clusters in the FCM algorithm for automatic multispectral image clustering. The performance of our proposed algorithm is first tested on clustering a very high resolution aerial image for various numbers of clusters and, next, on clustering two very high resolution aerial images, a high resolution Worldview2 satellite image, a Landsat8 satellite image and an EO-1 hyperspectral image, for a constant number of clusters. The superiority of the new method is demonstrated by comparing it with the well-known methods of FCM, K-means, fast global FCM (FGFCM) and kernelized fast global FCM (KFGFCM) clustering algorithms, both quantitatively by calculating the DB, XB and SC indices and qualitatively by visualizing the cluster results.

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations. Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data, which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects. After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals, an interval-valued fuzzy c-means (FCM) clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study. Such a process can significantly improve the clustering effect; specifically, the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results. Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region, China, and the TM sensor for Yushu, Qinghai, China, were conducted, as well as experiments involving the conventional FCM algorithm, the results of which were used for comparative analysis. Next, a supervised classification method was used to validate the clustering results. The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.

A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation

The fuzzy C-means (FCM) algorithm has significant importance compared to other methods in Medical image segmentation. Conventional FCM algorithm is sensitive to noise especially in the presence of intensity inhomogeneity in MRI. Main reason is that a single fuzzifier in FCM cannot properly represent pattern memberships for all clusters. In this paper, we present a novel algorithm for fuzzy segmentation of MRI data. The algorithm utilizes two fuzzifiers used in interval type-2 FCM and a spatial constraint on the membership functions. Also, in our investigation, validity functions are extended to generalized form for interval type-2 fuzzy clustering. The experimental results on both synthetic and MR images show that the proposed algorithm has better performance on image segmentation than conventional FCM based algorithms.

Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation

This paper presents a fuzzy clustering-based technique for image segmentation. Many attempts have been put into practice to increase the conventional fuzzy c-means (FCM) performance. In this paper, the sensitivity of the soft membership function of the FCM algorithm to the outlier is considered and the new exponent operator on the Euclidean distance is implemented in the membership function to improve the outlier rejection characteristics of the FCM. The comparative quantitative and qualitative studies are performed among the conventional k-means (KM), moving KM, and FCM algorithms; the latest state-of-the-art clustering algorithms, namely the adaptive fuzzy moving KM , adaptive fuzzy KM, and new weighted FCM algorithms; and the proposed outlier rejection FCM (ORFCM) algorithm. It is revealed from the experimental results that the ORFCM algorithm outperforms the other clustering algorithms in various evaluation functions.

Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation

Image segmentation is one of the significant computational applications of the biomedical field. Automated computational methodologies are highly preferred for medical image segmentation since these techniques are immune to human perception error. Artificial intelligence (AI)-based techniques are often used for this process since they are superior to other automated techniques in terms of accuracy and convergence time period. Fuzzy systems hold a significant position among the AI techniques because of their high accuracy. Even though these systems are exceptionally accurate, the time period required for convergence is exceedingly high. In this work, a novel distance metric-based fuzzy C-means (FCM) algorithm is proposed to tackle the low-convergence-rate problem of the conventional fuzzy systems. This modified approach involves the concept of distance-based dimensionality reduction of the input vector space that substantially reduces the iterative time period of the conventional FCM algorithm. The effectiveness of the modified FCM algorithm is explored in the context of magnetic resonance brain tumor image segmentation. Experimental results show promising results for the proposed approach in terms of convergence time period and segmentation efficiency. Thus, this algorithm proves to be highly feasible for time-oriented real-time applications.

Optimized Fuzzy Logic Application for MRI Brain Images Segmentation

In this paper, an optimized fuzzy logic method for Magnetic Resonance Imaging (MRI) brain images segmentation is presented. The method is a technique based on a modified fuzzy c-means (FCM) clustering algorithm. The FCM algorithm that incorporates spatial information into the membership function is used for clustering, while a conventional FCM algorithm does not fully utilize the spatial information in the image. The advantages of the algorithm are that it is less sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. Originality of this research is the methods applied on a normal MRI brain image and MRI brain images with tumor, and analyze the area of tumor from segmented images. The results show that the method effectively segmented MRI brain images with spatial information, and the segmented MRI normal brain image and MRI brain images with tumor can be analyzed for diagnosis purpose. In order to identify the area of abnormal mass of MRI brain images with tumor, it is resulted that the area is identified from 8.38 to 25.57 cm 2 .

Color Image Segmentation using Fast Fuzzy C-Means Algorithm

This paper proposes modified FCM (Fuzzy C-means) approach to color image segmentation using JND (Just Noticeable Difference) histogram. Histogram of the given color image is computed using JND color model. This samples the color space so that just enough number of histogram bins are obtained on each axis without compromising the visual image content. The number of histogram bins are further reduced using agglomeration. This agglomerated histogram yields the estimation of number of clusters, cluster seeds and the initial fuzzy partition for FCM algorithm. This is a novell approach to estimate the input parameters for FCM algorithm. Then the modified FCM algorithm is proposed that works on histogram bins as data elements instead of individual pixels. This significantly reduces the time complexity of FCM algorithm. To verify the effectiveness of the proposed image segmentation approach, its performance is evaluated on Berkeley Segmentation Database(BSD). Two significant criterias namely PSNR and PRI (Probabilistic Rand Index) are used to evaluate the performance. Results show that the proposed algorithm applied to the JND histogram bins converges much faster and also gives better results than conventional FCM algorithm in terms of PSNR and PRI.

Fuzzy Expectation Maximum Algorithm for Magnetic Resonance Image Segmentation

Fuzzy clustering algorithm especially the fuzzy c-means (FCM) algorithm has been widely used for segmentation of brain magnetic resonance (MR) images. However, the conventional FCM algorithm has a very serious shortcoming, i.e., the algorithm tends to balance the number of points in each cluster during the classification. Therefore, when this algorithm is applied to segment the MR images with quite different size of objects, it will lead to bad segmentation. To overcome this problem, a novel fuzzy expectation maximization (FEM) algorithm is presented in this paper. The algorithm is developed by extending the classical hard EM algorithm into soft EM algorithm through integrating the fuzzy and statistical techniques. Compared with the FCM algorithm, the experimental results on MR image segmentation clearly indicate that the proposed FEM algorithm has better performance for the segmentation.

Novel modified fuzzy c-means algorithm with applications

Fuzzy c-means (FCM) clustering algorithm has been widely used in many medical image segmentations. However, the conventionally standard FCM algorithm is noise sensitive because of not taking into account the spatial information. To overcome the above problem, a novel modified FCM algorithm (called FCM-AWA later) for image segmentation is presented in this paper. The algorithm is realized by modifying the objective function in the conventional FCM algorithm, i.e., by incorporating the spatial neighborhood information into the standard FCM algorithm. An adaptive weighted averaging (AWA) filter is given to indicate the spatial influence of the neighboring pixels on the central pixel. The parameters (weighting coefficients) of control template (neighboring widow) are automatically determined in the implementation of the weighted averaging image by a predefined nonlinear function. The presented algorithm is applied to both artificial synthesized image and real image. Furthermore, the quantifications of dental plaque using proposed algorithm-based segmentation were conducted. Experimental results show that the presented algorithm performs more robust to noise than the standard FCM algorithm and another FCM algorithm (proposed by Ahmed) do. Furthermore, the results of dental plaque quantification using proposed method indicate the FCM-AWA provides a quantitative, objective and efficient analysis of dental plaque, and possesses great promise.

Automatic Segmentation of Large Power Systems Into Fuzzy Coherent Areas for Dynamic Vulnerability Assessment

This paper proposes a technique for partitioning a large power system into a number of coherent electric areas for possible application to dynamic vulnerability assessment. The coherency concept and a fuzzy clustering algorithm grouping of buses are combined to achieve this goal. The clusters are obtained by selecting representative buses from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. The initialization problem of the conventional fuzzy c-means algorithm, which usually leads to multiple solutions, is suitably tackled by incorporating the maximum-dissimilarity based sequential phasor measurement unit (PMU) placement technique. Results of bus grouping for two test systems of three and nine areas demonstrate the potential of the approach. It is observed that such an approach to bus grouping results in a PMU configuration with minimum number of devices and fast data aggregation for a wide-area measurement system.

Fuzzy c-means clustering with spatial information for image segmentation

A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information.

FUZZY CLUSTERING WITH GENETICALLY ADAPTIVE SCALING

In this paper we present a genetically enhanced version of the classical fuzzy c-means clustering algorithm. Our algorithm uses an evolutionary method to find optimal values for some scaling constants which are used to scale the various dimensions of the given data set so that clusters can be more easily detected by compensating for differences in distributions among features. We demonstrate how using un-scaled data with the conventional fuzzy c-means algorithm can lead to incorrect classification and how our algorithm overcomes the problem. We present the results of applying our method to both a synthetic data set, which we created to demonstrate the problem, and the standard Iris data set. In both cases, reduction of misclassifications was obtained by the new method, demonstrating improvement over the standard fuzzy c-means algorithm.

Fuzzy image clustering incorporating spatial continuity

The authors present a spatial fuzzy clustering algorithm that exploits the spatial contextual information in image data. The objective functional of their method utilises a new dissimilarity index that takes into account the influence of the neighbouring pixels on the centre pixel in a 3×3 window. The algorithm is adaptive to the image content in the sense that influence from the neighbouring pixels is suppressed in nonhomogeneous regions in the image. A cluster merging scheme that merges two clusters based on their closeness and their degree of overlap is presented. Through this merging scheme, an `optimal' number of clusters can be determined automatically as iteration proceeds. Experimental results with synthetic and real images indicate that the proposed algorithm is more tolerant to noise, better at resolving classification ambiguity and coping with different cluster shape and size than the conventional fuzzy c-means algorithm.