Fuzzy C-means Membership Functions Research Articles

A novel non-disjoint partitioning algorithm for inter-cluster communication in wireless sensor networks

Clustering accomplishes energy efficiency, seamless communication, and quality of service (QoS) metrics in wireless sensor networks. The idea of assimilating an overlapping method with clustering presents a virtuous inter-cluster communication in a distributed hostile environment. Therefore, a novel unequal non-disjoint partitioning algorithm called ratings based energy-efficient clustering overlap (REEC overlap) is proposed using K harmonic means (KHM) and fuzzy C means (FCM). The clusters are overlapped by combining the harmonic average distance, and FCM membership function. The cluster heads are chosen and rotated by rating value in which intersecting nodes are eliminated. The route is discovered using the energy required threshold with the least distance of nodes wherein frequency division multiple access (FDMA) is espoused as a data transmission medium. Simulation results show that the proposed work performs better than existing protocols. Moreover, REEC overlap is compared with REEC non-overlap to illustrate the energy dissipation of cluster heads that was proposed in the previous work.

Analysis of Different Fuzzy C-Means Membership Functions for the Design of CAD Tool of Early Breast Cancer Detection

In this paper, analysis of the different membership function for fuzzy C-means based segmentation technique are done to overcome the issue of random initialization of membership matrix, 116 hybrid features set and analysis of various classifier with modified K nearest neighbor (KNN) are proposed for primary breast cancer detection. The steps involved to design CAD tool for breast cancer detection includes preprocessing, segmentation, feature extraction, selection, and classification. In pre-processing, manual cropping of original mammograms for removal of background details, and contrast enhancements are used. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancement of mammographic images. Further, nonlinear complex diffusion based unsharp-masking and crispening method is utilized to highlight the abnormalities of mammogram such as micro calcifications, tumors, etc. Further, 2D DWT is applied for taking approximation coefficient followed by fuzzy C-means segmentation with different types of fuzzy membership functions, which is utilized to segment and extract the region of interest. For feature extraction, 116 hybrid features are extracted from each image such as Geometric based features, Texture based features, Gabor based features and Histogram based features. Next step is Minimum Redundancy and Maximum Relevancy (MRMR) is used as features selection technique for discarding repetitive and irrelevant features. Final step is classification, the modify KNN classifier based on mutual information in place of Euclidean distance is proposed while finding the nearest neighbors. The comparative analysis of the fuzzy C-means segmentation techniques with different types of fuzzy membership methods are presented in terms of RI, GCE, VOI and the various classifier performance are taken in terms of accuracy, precision, sensitivity, specificity.

Intelligent Technique for Signal Processing to Identify the Brain Disorder for Epilepsy Captures Using Fuzzy Systems

The new direction of understand the signal that is created from the brain organization is one of the main chores in the brain signal processing. Amid all the neurological disorders the human brain epilepsy is measured as one of the extreme prevalent and then programmed artificial intelligence detection technique is an essential due to the crooked and unpredictable nature of happening of epileptic seizures. We proposed an Improved Fuzzy firefly algorithm, which would enhance the classification of the brain signal efficiently with minimum iteration. An important bunching technique created on fuzzy logic is the Fuzzy C means. Together in the feature domain with the spatial domain the features gained after multichannel EEG signals remained combined by means of fuzzy algorithms. And for better precision segmentation process the firefly algorithm is applied to optimize the Fuzzy C-means membership function. Simultaneously for the efficient clustering method the convergence criteria are set. On the whole the proposed technique yields more accurate results and that gives an edge over other techniques. This proposed algorithm result compared with other algorithms like fuzzy c means algorithm and PSO algorithm.

Dynamic Incorporation ofWavelet Filter in Fuzzy C-Means for Efficient and Noise-Insensitive MR Image Segmentation

AbstractImage intensity in magnetic resonance (MR) images in the presence of noise obeys Rician distribution. The signal-dependent Rician noise makes accurate image segmentation a challenging task. Although existing fuzzy c-means (FCM) variants with local filters improve the segmentation performance, they are less effective for reducing the negative effect from Rician noise, and the repeatedly applied filter increases their computational intensiveness. To address this issue, we propose a novel image segmentation method which dynamically incorporates wavelet-based noise detector and filter in the FCM membership function. The modified algorithm is designed to exploit both frequency and spatial information in the images and minimizes clustering errors caused by Rician noise. Furthermore, efficiency of the proposed method can be enhanced by the strategy of applying filter only when noise is detected. The experimental results of segmentation on synthetic and brain MR images, demonstrate the computational effic...

A robust elicitation algorithm for discovering DNA motifs using fuzzy self-organizing maps.

It is important to identify DNA motifs in promoter regions to understand the mechanism of gene regulation. Computational approaches for finding DNA motifs are well recognized as useful tools to biologists, which greatly help in saving experimental time and cost in wet laboratories. Self-organizing maps (SOMs), as a powerful clustering tool, have demonstrated good potential for problem solving. However, the current SOM-based motif discovery algorithms unfairly treat data samples lying around the cluster boundaries by assigning them to one of the nodes, which may result in unreliable system performance. This paper aims to develop a robust framework for discovering DNA motifs, where fuzzy SOMs, with an integration of fuzzy c-means membership functions and a standard batch-learning scheme, are employed to extract putative motifs with varying length in a recursive manner. Experimental results on eight real datasets show that our proposed algorithm outperforms the other searching tools such as SOMBRERO, SOMEA, MEME, AlignACE, and WEEDER in terms of the F-measure and algorithm reliability. It is observed that a remarkable 24.6% improvement can be achieved compared to the state-of-the-art SOMBRERO. Furthermore, our algorithm can produce a 20% and 6.6% improvement over SOMBRERO and SOMEA, respectively, in finding multiple motifs on five artificial datasets.

Automatic quantitative analysis of in‐stent restenosis using FD‐OCT in vivo intra‐arterial imaging

A new segmentation technique is implemented for automatic lumen area extraction and stent strut detection in intravascular optical coherence tomography (OCT) images for the purpose of quantitative analysis of in-stent restenosis (ISR). In addition, a user-friendly graphical user interface (GUI) is developed based on the employed algorithm toward clinical use. Four clinical datasets of frequency-domain OCT scans of the human femoral artery were analyzed. First, a segmentation method based on fuzzy C means (FCM) clustering and wavelet transform (WT) was applied toward inner luminal contour extraction. Subsequently, stent strut positions were detected by utilizing metrics derived from the local maxima of the wavelet transform into the FCM membership function. The inner lumen contour and the position of stent strut were extracted with high precision. Compared to manual segmentation by an expert physician, the automatic lumen contour delineation had an average overlap value of 0.917 ± 0.065 for all OCT images included in the study. The strut detection procedure achieved an overall accuracy of 93.80% and successfully identified 9.57 ± 0.5 struts for every OCT image. Processing time was confined to approximately 2.5 s per OCT frame. A new fast and robust automatic segmentation technique combining FCM and WT for lumen border extraction and strut detection in intravascular OCT images was designed and implemented. The proposed algorithm integrated in a GUI represents a step forward toward the employment of automated quantitative analysis of ISR in clinical practice.

A Novel Clustering and Classification based Approaches for Identifying Tumor in MRI Brain Images

In image processing, image segmentation is one of the important tasks to extract information from the images. A variety of segmentation algorithm is developed to satisfy increasing requirement of image segmentation. Fuzzy CMeans is unsupervised method that has been applied for the variety of purposes such as clustering, classification, image segmentation and target recognition. This method can classify the image, which can be represented in various feature spaces by grouping the similar data points in the feature space into clusters. Especially the FCM can be used to obtain the segmentation with the pixel classification where this method allows pixels to be the property of multiple classes with varying degree of membership. This method can produce the flexibility in processing of Magnetic Resonance Image (MRI). In our proposed first, proposing the K-Means with FCM method and color model to improve the existing system. This algorithm is based on maximum measure of the distance function which is found for cluster center detection process using the Mahalanobis concept. The objective of this research is to develop an enhanced k-means and fuzzy c-means for a segmentation of brain magnetic resonance images. Also we are implementing the Fuzzy Membership based function to select the initial centers for the segmentation process. The firefly algorithm is implemented to optimize the Fuzzy Cmeans membership function for better accuracy segmentation process. At the same time the convergence criteria is fixed for the efficient clustering method. On the whole the proposed technique produces more accurate results compare with other techniques.

SU-E-I-90: Fast and Robust Algorithm Towards Vessel Lumen and Stent Strut Detection in Optical Coherence Tomography.

Optical Coherence Tomography (OCT) is a catheter-based imaging method that employs near-infrared light to produce high-resolution cross-sectional intravascular images. We propose a new segmentation technique for automatic lumen area extraction and stent strut detection in intravascular OCT images for the purpose of quantitative analysis of neointimal hyperplasia (NIH). Two clinical dataset of frequency-domainOCT scans of the human femoral artery were analyzed. First, a segmentation method based on Fuzzy C-Means (FCM) clustering and Wavelet Transform (WT) was applied towards inner luminal contour extraction. Subsequently, stent strut positions were detected by utilizing metrics derived from the local maxima of the wavelet transform into the FCM membership function. The inner lumen contour and the position of stent strut were extracted with very high accuracy. Compared with manual segmentation by an expert physician, the automatic segmentation had an average overlap value of 0.917 ± 0.065 for all OCT images included in the study. The strut detection procedure successfully identified 6.7 ± 0.5 struts for each OCT image. A new fast and robust automatic segmentation technique combining FCM and WT for lumen border extraction and strut detection in intravascular OCT images was designed and implemented. The proposed algorithm may be employed for automated quantitative morphological analysis of in-stent neointimal hyperplasia.

Treatment response assessment of breast masses on dynamic contrast‐enhanced magnetic resonance scans using fuzzy ‐means clustering and level set segmentation

The goal of this study was to develop an automated method to segment breast masses on dynamic contrast-enhanced (DCE) magnetic resonance (MR) scans and to evaluate its potential for estimating tumor volume on pre- and postchemotherapy images and tumor change in response to treatment. A radiologist experienced in interpreting breast MR scans defined a cuboid volume of interest (VOI) enclosing the mass in the MR volume at one time point within the sequence of DCE-MR scans. The corresponding VOIs over the entire time sequence were then automatically extracted. A new 3D VOI representing the local pharmacokinetic activities in the VOI was generated from the 4D VOI sequence by summarizing the temporal intensity enhancement curve of each voxel with its standard deviation. The method then used the fuzzy c-means (FCM) clustering algorithm followed by morphological filtering for initial mass segmentation. The initial segmentation was refined by the 3D level set (LS) method. The velocity field of the LS method was formulated in terms of the mean curvature which guaranteed the smoothness of the surface, the Sobel edge information which attracted the zero LS to the desired mass margin, and the FCM membership function which improved segmentation accuracy. The method was evaluated on 50 DCE-MR scans of 25 patients who underwent neoadjuvant chemotherapy. Each patient had pre- and postchemotherapy DCE-MR scans on a 1.5 T magnet. The in-plane pixel size ranged from 0.546 to 0.703 mm and the slice thickness ranged from 2.5 to 4.5 mm. The flip angle was 15 degrees, repetition time ranged from 5.98 to 6.7 ms, and echo time ranged from 1.2 to 1.3 ms. Computer segmentation was applied to the coronal T1-weighted images. For comparison, the same radiologist who marked the VOI also manually segmented the mass on each slice. The performance of the automated method was quantified using an overlap measure, defined as the ratio of the intersection of the computer and the manual segmentation volumes to the manual segmentation volume. Pre- and postchemotherapy masses had overlap measures of 0.81 +/- 0.13 (mean +/- s.d.) and 0.71 +/- 0.22, respectively. The percentage volume reduction (PVR) estimated by computer and the radiologist were 55.5 +/- 43.0% (mean +/- s.d.) and 57.8 +/- 51.3%, respectively. Paired Student's t test indicated that the difference between the mean PVRs estimated by computer and the radiologist did not reach statistical significance (p = 0.641). The automated mass segmentation method may have the potential to assist physicians in monitoring volume change in breast masses in response to treatment.

A FUZZY APPROACH TO SPEAKER VERIFICATION

This paper proposes a fuzzy approach to speaker verification. For an input utterance and a claimed identity, most of the current methods compute a claimed speaker's score, which is the ratio of the claimed speaker's and the impostors' likelihood functions, and compare this score with a given threshold to accept or reject this speaker. Considering the speaker verification problem based on fuzzy set theory, the claimed speaker's score is viewed as the fuzzy membership function of the input utterance in the claimed speaker's fuzzy set of utterances. Fuzzy entropy and fuzzy c-means membership functions are proposed as fuzzy membership scores, which are the ratios of functions of the claimed speaker's and impostors' likelihood functions. A likelihood transformation is also considered to relate current likelihood and fuzzy membership scores. We also proposed fuzzy scores using membership functions similar to those produced by noise-clustering-based method. This noise clustering concept provides very effective modifications to several methods, which can overcome some of the problems of ratio-type scores and greatly reduce the false acceptance rate. Experiments were performed to evaluate proposed normalization methods for speaker verification using the YOHO corpus. Experiments demonstrate that fuzzy methods and their noise clustering versions outperform conventional methods.

The fuzzy c+2-means: solving the ambiguity rejection in clustering

In this paper we deal with the clustering problem whose goal consists of computing a partition of a family of patterns into disjoint classes. The method that we propose is formulated as a constrained minimization problem, whose solution depends on a fuzzy objective function in which reject options are introduced. Two types of rejection have been included: the ambiguity rejection which concerns patterns lying near the class boundaries and the distance rejection dealing with patterns that are far away from all the classes. To compute these rejections, we propose an extension of the fuzzy c-means (FcM) algorithm of Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981. This algorithm is called the fuzzy c+2-means (Fc+2M). These measures allow to manage uncertainty due both to imprecise and incomplete definition of the classes. The advantages of our method are (1) the degree of membership to the reject classes for a pattern x k are learned in the iterative clustering problem; (2) it is not necessary to compute other characteristics to determine the reject and ambiguity degrees; (3) the partial ambiguity rejections introduce a discounting process between the classical FcM membership functions in order to avoid the memberships to be spread across the classes; (4) the membership functions are more immune to noise and correspond more closely to the notion of compatibility. Preliminary computational experiences on the developed algorithm are encouraging and compared favorably with results from other methods as FcM, FPcM and F(c+1)M (fuzzy c+1-means: clustering with solely distance rejection) algorithms on the same data sets. The differences in the performance can be attributed to the fact that ambiguous patterns are less accounted in for the computing of the centers.

ON EXISTENCE AND STRONG CONSISTENCY OF A CLASS OF FUZZYC-MEANS CLUSTERING PROCEDURES

In this paper the existence and strong consistency of a class of fuzzy c-means (FCM) clustering procedures are established. Suppose that the data set is a simple random sample of observations from a probability distribution, assuming that the second moment of the distribution is finite. Then the solution to the FCM clustering procedures shall exist. The FCM cluster centers and the FCM membership functions will have the property of strong consistency. That is, the sample FCM cluster centers and the population FCM cluster centers will be close to each other with probability one, and also the sample FCM membership functions and the population FCM membership functions will be close to each other with probability one when the sample size increases to infinity.