Adaptive Fuzzy C-means Research Articles

Adaptive Kernel-Based Fuzzy C-Means Clustering with Spatial Constraints for Image Segmentation

In order to resolve the disadvantages of fuzzy C-means (FCM) clustering algorithm for image segmentation, an improved Kernel-based fuzzy C-means (KFCM) clustering algorithm is proposed. First, the reason why the kernel function is introduced is researched on the basis of the classical KFCM clustering. Then, using spatial neighborhood constraint property of image pixels, an adaptive weighted coefficient is introduced into KFCM to control the influence of the neighborhood pixels to the central pixel automatically. At last, a judging rule for partition fuzzy clustering numbers is proposed that can decide the best clustering partition numbers and provide an optimization foundation for clustering algorithm. An adaptive kernel-based fuzzy C-means clustering with spatial constraints (AKFCMS) model for image segmentation approach is proposed in order to improve the efficiency of image segmentation. Various experiment results show that the proposed approach can get the spatial information features of an image accurately and is robust to realize image segmentation.

Soil pore identification with the adaptive fuzzy C-means method based on computed tomography images

The complex geometry and topology of soil is widely recognised as the key driver in many ecological processes. X-ray computed tomography (CT) provides insight into the internal structure of soil pores automatically and accurately. Until recently, there have not been methods to identify soil pore structures. This has restricted the development of soil science, particularly regarding pore geometry and spatial distribution. Through the adoption of the fuzzy clustering theory and the establishment of pore identification rules, a novel pore identification method is described to extract pore structures from CT soil images. The robustness of the adaptive fuzzy C-means method (AFCM), the adaptive threshold method, and Image-Pro Plus tools were compared on soil specimens under different conditions, such as frozen, saturated, and dry situations. The results demonstrate that the AFCM method is suitable for identifying pore clusters, especially tiny pores, under various soil conditions. The method would provide an optional technique for the study of soil micromorphology.

基于自适应模糊C均值与后处理的图像分割算法

基于自适应模糊C均值与后处理的图像分割算法

Segmentation of multicolor fluorescence in situ hybridization images using an improved fuzzy C-means clustering algorithm by incorporating both spatial and spectral information.

Multicolor fluorescence in situ hybridization (M-FISH) is a multichannel imaging technique for rapid detection of chromosomal abnormalities. It is a critical and challenging step to segment chromosomes from M-FISH images toward better chromosome classification. Recently, several fuzzy C-means (FCM) clustering-based methods have been proposed for M-FISH image segmentation or classification, e.g., adaptive fuzzy C-means (AFCM) and improved AFCM (IAFCM), but most of these methods used only one channel imaging information with limited accuracy. To improve the segmentation for better accuracy and more robustness, we proposed an FCM clustering-based method, denoted by spatial- and spectral-FCM. Our method has the following advantages: (1)it is able to exploit information from neighboring pixels (spatial information) to reduce the noise and (2)it can incorporate pixel information across different channels simultaneously (spectral information) into the model. We evaluated the performance of our method by comparing with other FCM-based methods in terms of both accuracy and false-positive detection rate on synthetic, hybrid, and real images. The comparisons on 36 M-FISH images have shown that our proposed method results in higher segmentation accuracy ([Formula: see text]) and a lower false-positive ratio ([Formula: see text]) than conventional FCM (accuracy: [Formula: see text], and false-positive ratio: [Formula: see text]) and the IAFCM (accuracy: [Formula: see text] and false-positive ratio: [Formula: see text]) methods by incorporating both spatial and spectral information from M-FISH images.

Adaptive fuzzy c-means clustering algorithm for interval data type based on interval-dividing technique

Clustering for symbolic data type is a necessary process in many scientific disciplines, and the fuzzy c-means clustering for interval data type (IFCM) is one of the most popular algorithms. This paper presents an adaptive fuzzy c-means clustering algorithm for interval-valued data based on interval-dividing technique. This method gives a fuzzy partition and a prototype for each fuzzy cluster by optimizing an objective function. And the adaptive distance between the pattern and its cluster center varies with each algorithm iteration and may be either different from one cluster to another or the same for all clusters. The novel part of this approach is that it takes into account every point in both intervals when computing the distance between the cluster and its representative. Experiments are conducted on synthetic data sets and a real data set. To compare the comprehensive performance of the proposed method with other four existing methods, the corrected rand index, the value of objective function and iterations are introduced as the evaluation criterion. Clustering results demonstrate that the algorithm proposed in this paper has remarkable advantages.

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.

An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images.

Ultrasound (US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis (CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means (FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 ± 5.33%, 97.83 ± 2.17%, 86.38 ± 5.80%, and 92.58 ± 3.68%, respectively. The region-based metrics of average symmetric surface distance (ASSD), root mean square symmetric distance (RMSD), and maximum symmetric surface distance (MSSD) were 0.03 ± 0.04 mm, 0.04 ± 0.03 mm, and 1.18 ± 1.01 mm, respectively. All the metrics except sensitivity were better than that of the non-adaptive algorithm and the conventional FCM. Only three region-based metrics were better than that of the kernel-induced distance based FCM with spatial constraints. Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the potential application of the method in breast tumor CAD and other US-guided procedures.

Recognition of High-Voltage Cable Partial Discharge Signal Based on Adaptive Fuzzy C-Means Clustering

Partial discharge (PD) detection is an effective means to find high-voltage cable defects. However, various interference signals affect the PD signal in practical applications, resulting in wrong judgment. In order to improve the accuracy of PD detection of high voltage cables, an adaptive fuzzy C-means (FCM) clustering was proposed to identify PD signals. The adaptive threshold pulse extraction algorithm based on fixed interval width was employed. The threshold was changed adaptively to extract the effective PD pulse waveform according to the change of the background noise and the degree of PD. Then PD pulse features were analyzed in time and frequency domain by employing the equivalent time frequency analysis method. The adaptive fuzzy clustering algorithm was used to classify the signals. The phase distribution concentration of all kinds of pulse signal was calculated. The phase standard deviation of various types of pulse was taken as the index that measures the concentration of density to distinguish between PD and interference signals. Results show that the adaptive FCM clustering algorithm, compared with the traditional method, can not only identify the PD signal accurately, but also be conscious of the PD category. The PD recognition method proposed in this paper has strong applicability and high accuracy, which is particularly suitable for application in the field of engineering.

Novel fuzzy clustering scheme for 3D wireless sensor networks

In this paper, we consider the sensor-energy optimization in 3D Wireless Sensor Networks (WSNs), which determines an optimal topology of sensors to prolong the network lifetime and reduce the energy expenditure. A new mathematical model for clustering in 3D WSN, considering energy consumption, constraints of communication and a 3D energy function, is presented. Using the Lagrange multiplier method, solutions of the model consisting of cluster centres and the membership matrix are computed and used in the new algorithm, called FCM-3 WSN. Experimental validation on real 3D datasets demonstrates that FCM-3 WSN outperforms the relevant methods, namely, Low-Energy Adaptive Clustering Hierarchy (LEACH), Centralized LEACH (LEACH-C), Single-hop Clustering and Energy-Efficient Protocol (SCEEP), Hybrid-Low Energy Adaptive Clustering Hierarchy (H-LEACH), K-Means and Fuzzy C-Means (FCM).

Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting.

Automatic segmentation of particular heart parts plays an important role in recognition tasks, which is utilized for diagnosis and treatment. One particularly important application is segmentation of epicardial fat (surrounds the heart), which is shown by various studies to indicate risk level for developing various cardiovascular diseases as well as to predict progression of certain diseases. Quantification of epicardial fat from CT images requires advance image segmentation methods. The problem of the state-of-the-art methods for epicardial fat segmentation is their high dependency on user interaction, resulting in low reproducibility of studies and time-consuming analysis. We propose in this paper a novel semiautomatic approach for segmentation and quantification of epicardial fat from 3D CT images. Our method is a semisupervised slice-by-slice segmentation approach based on local adaptive morphology and fuzzy c-means clustering. Additionally, we use a geometric ellipse prior to filter out undesired parts of the target cluster. The validation of the proposed methodology shows good correspondence between the segmentation results and the manual segmentation performed by physicians.

An adaptive clustering segmentation algorithm based on FCM

The cluster number and the initial clustering centers must be reasonably set before the analysis of clustering in most cases. Traditional clustering segmentation algorithms have many shortcomings, such as high reliance on the specially established initial clustering center, tendency to fall into the local maximum point, and poor performance with multithreshold values. To overcome these defects, an adaptive fuzzy C-means segmentation algorithm based on a histogram (AFCMH), which synthesizes both main peaks of the histogram and optimized Otsu criterion, is proposed. First, the main peaks of the histogram are chosen by operations like histogram smoothing, merging of adjacent peaks, and filtering of small peaks, and then the values of main peaks are calculated. Second, a new separability measure $\eta $ is defined and a group of main peaks with the maximum value of $\eta $ serve as the optimal segmentation threshold value. The values of these main peaks are employed for initializing of the initial clustering center. Finally, the image is segmented by the weighted fuzzy C-means clustering algorithm. The experiment results show that, compared with existing algorithms, the proposed method not only avoids the oversegmentation phenomenon but also has a significantly shorter computing time than the traditional segmentation algorithm based on mean shift. Therefore, the proposed algorithm can obtain satisfactory results and effectively improve executive efficiency.

Multiobjective Adaptive Fuzzy C-Means Algorithm for Mining Frequent Closed Itemset: A Lattice Based Approach for Time Series Data

Of late, frequent itemset mining from the data bases has emerged as a highly challenging task. And, there is a feast of various approaches launched with an eye on extracting the frequent rules from the database. In the erstwhile frequent itemset mining technique, the frequent itemset are extracted from the time series data by means of Adaptive Fuzzy C-Means (AFCM) clustering, though it is saddled with several setbacks in the clustering procedure. This arises on account of the fact that in clustering, the number of itemset is mammoth, and hence the method consumes a lot of time in addition to spending enhanced scanning time. Therefore, with a view tackling these adverse features, an innovative frequent itemset mining method is suggested in this document. At the outset, the time sequence database values are grouped by means of the Multiobjective AFCM method. Subsequently, the frequent itemset are extracted from the grouped outcomes by employing the lattice based method. The novel technique is performed in the MATLAB platform and its excellence is appraised by means of rainfall database.

A patch-based tensor decomposition algorithm for M-FISH image classification.

Multiplex-fluorescence in situ hybridization (M-FISH) is a chromosome imaging technique which can be used to detect chromosomal abnormalities such as translocations, deletions, duplications, and inversions. Chromosome classification from M-FISH imaging data is a key step to implement the technique. In the classified M-FISH image, each pixel in a chromosome is labeled with a class index and drawn with a pseudo-color so that geneticists can easily conduct diagnosis, for example, identifying chromosomal translocations by examining color changes between chromosomes. However, the information of pixels in a neighborhood is often overlooked by existing approaches. In this work, we assume that the pixels in a patch belong to the same class and use the patch to represent the center pixel's class information, by which we can use the correlations of neighboring pixels and the structural information across different spectral channels for the classification. On the basis of assumption, we propose a patch-based classification algorithm by using higher order singular value decomposition (HOSVD). The developed method has been tested on a comprehensive M-FISH database that we established, demonstrating improved performance. When compared with other pixel-wise M-FISH image classifiers such as fuzzy c-means clustering (FCM), adaptive fuzzy c-means clustering (AFCM), improved adaptive fuzzy c-means clustering (IAFCM), and sparse representation classification (SparseRC) methods, the proposed method gave the highest correct classification ratio (CCR), which can translate into improved diagnosis of genetic diseases and cancers. © 2016 International Society for Advancement of Cytometry.

Adaptive fuzzy c‐means algorithm based on local noise detecting for image segmentation

Adding spatial penalty terms in fuzzy c-means (FCM) models is an important approach for reducing the noise effects in the process of image segmentation. Though these algorithms have improved the robustness to noises in a certain extent, they still have some shortcomings. First, they are usually very sensitive to the parameters which are supposed to be tuned according to noise intensities. Second, in the case of inhomogeneous noises, using a constant parameter for different image regions is obviously unreasonable and usually leads to an unideal segmentation result. For overcoming these drawbacks, a noise detecting-based adaptive FCM for image segmentation is proposed in this study. Two image filtering methods, playing the roles of denoising and maintaining detail information are utilised in the new algorithm. The parameters for balancing these two parts are computed by measuring the variance of grey-level values in each neighbourhood. Numerical experiments on both synthetic and real-world image data show that the new algorithm is effective and efficient.

A dynamic fuzzy genetic algorithm for natural image segmentation using adaptive mean shift

In this paper, a colour image segmentation approach based on hybridisation of adaptive mean shift (AMS), fuzzy c-mean and genetic algorithms (GAs) is presented. Image segmentation is the perceptual faction of pixels based on some likeness measure. GA with fuzzy behaviour is adapted to maximise the fuzzy separation and minimise the global compactness among the clusters or segments in spatial fuzzy c-mean (sFCM). It adds diversity to the search process to find the global optima. A simple fusion method has been used to combine the clusters to overcome the problem of over segmentation. The results show that our technique outperforms state-of-the-art methods.

Identification of protein sequence motifs using modified and adaptive fuzzy C-means granular models

The activities and functions of proteins are determined by protein sequence motifs. All protein sequence segments may not produce potential motif patterns. The generated sequence segments have no standard labels. Hence, unsupervised segment selection technique is adopted to select significant protein sequence segments. Therefore, Singular Value Decomposition SVD entropy is used to select potential sequence segments. In this proposed work, SVD is combined with two different types of Granular Computing models including Modified Fuzzy C-Means and Adaptive Fuzzy C-Means to generate protein motif information efficiently. The two proposed models are compared with Fuzzy C-Means granular computing model. The experimental results show that Adaptive Fuzzy C-Means granular technique outperforms Modified Fuzzy C-Means and Fuzzy C-Means. A new evaluation method for sequence motif information namely 'Information-Gain' measure is adopted.

Identification of protein sequence motifs using modified and adaptive fuzzy C-means granular models

The activities and functions of proteins are determined by protein sequence motifs. All protein sequence segments may not produce potential motif patterns. The generated sequence segments have no standard labels. Hence, unsupervised segment selection technique is adopted to select significant protein sequence segments. Therefore, Singular Value Decomposition (SVD) entropy is used to select potential sequence segments. In this proposed work, SVD is combined with two different types of Granular Computing models including Modified Fuzzy C-Means and Adaptive Fuzzy C-Means to generate protein motif information efficiently. The two proposed models are compared with Fuzzy C-Means granular computing model. The experimental results show that Adaptive Fuzzy C-Means granular technique outperforms Modified Fuzzy C-Means and Fuzzy C-Means. A new evaluation method for sequence motif information namely 'Information-Gain' measure is adopted.

Kernelized Fuzzy C-means Clustering with Adaptive Thresholding for Segmenting Liver Tumors

Accurate liver segmentation from computed tomography (CT) scan images is an essential and crucial step in computer-aided diagnosis of liver tumor. The automatic and unsupervised methods are used to segment CT image that reduce the time and eliminate the need for manual interface. In this paper, the adaptive threshold, morphological processing, and kernel fuzzy C-means (KFCM) clustering algorithm have been used with spatial information for visualizing and measuring the tumor area of liver which is segmented from CT abdominal images. The proposed process was tested and evaluated to a series of test images on MICCAI 2008 liver tumor segmentation challenge datasets to extract the tumor region. The area of the tumor reason has been calculated also. The KFCM algorithm introduces a kernel function on fuzzy c-means clustering (FCM) to reduce the effect of noise and improves the ability of clustering. Experimental results showed positive results for the proposed algorithm. The PSNR and MSE have been calculated for FCM and KFCM method to compare the results to segment the images. An experimental result shows that the proposed method could be used effectively to segment the liver cancer region in terms of high PSNR and low MSE values and achieves high consistency to detect small changes in images.

The Comparison of Fuzzy Clustering Methods for Symbolic Interval-Valued Data

Interval-valued data can find their practical applications in such situations as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. The primary objective of the presented paper is to compare three different methods of fuzzy clustering for interval-valued symbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering and fuzzy k-means clustering with fuzzy spectral clustering. Fuzzy spectral clustering combines both spectral and fuzzy approaches in order to obtain better results (in terms of Rand index for fuzzy clustering). The conducted simulation studies with artificial and real data sets confirm both higher usefulness and more stable results of fuzzy spectral clustering method, as compared to other existing fuzzy clustering methods for symbolic interval-valued data, when dealing with data featuring different cluster structures, noisy variables and/or outliers.

Exploratory Method for Spatio-Temporal Feature Extraction and Clustering: An Integrated Multi-Scale Framework

This paper presents an integrated framework for exploratory multi-scale spatio-temporal feature extraction and clustering of spatio-temporal data. The framework combines the multi-scale spatio-temporal decomposition, feature identification, feature enhancing and clustering in a unified process. The original data are firstly reorganized as multi-signal time series, and then decomposed by the multi-signal wavelet. Exploratory data analysis methods, such as histograms, are used for feature identification and enhancing. The spatio-temporal evolution process of the multi-scale features can then be tracked by the feature clusters based on the data adaptive Fuzzy C-Means Cluster. The approach was tested with the global 0.25° satellite altimeter data over a period of 21 years from 1993 to 2013. The tracking of the multi-scale spatio-temporal evolution characteristics of the 1997–98 strong El Niño were used as validation. The results show that our method can clearly reveal and track the spatio-temporal distribution and evolution of complex geographical phenomena. Our approach is efficient for global scale data analysis, and can be used to explore the multi-scale pattern of spatio-temporal processes.