Fuzzy C-means (FCM) is an unsupervised clustering technique and is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the Spatially Guided FCM (SGFCM) algorithm is presented which segments multi...

Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsup...

-This paper presents the application of fuzzy c-means (FCM) clustering in the order reduction of dynamic models for controller design in a power system. Based on the fuzzy c-means algorithm, a method is proposed for clustering the poles and zeros of the original power system model into new clusters from which a reduced-order model can be obtained. Then the reduced-order model is used to design ...

An improved segmentation Fuzzy C-Means algorithm (FCM) is proposed for the image recognition of transmission line insulators. In this paper, the improved Wiener filter algorithm is firstly used to filtrate and recover image in pre-processing. Then, the insulator image is segmented based on the improved algorithm FCM. Finally, the contour of insulator is labelled by using connected component lab...

In the process of image segmentation, the classic Fuzzy C-Means (FCM) algorithm is time-consuming and depends heavily on initialization center. Based on Graphic Processing Unit (GPU), this paper proposes a novel FCM algorithm by improving the computational formulas of membership degree and the update criterion of cluster centers. Our algorithm can initialize cluster centers purposefully and fur...

This paper introduces the Automated Two-Dimensional K-Means (A2DKM) algorithm, a novel unsupervised clustering technique. The proposed technique differs from the conventional clustering techniques because it eliminates the need for users to determine the number of clusters. In addition, A2DKM incorporates local and spatial information of the data into the clustering analysis. A2DKM is qualitati...

background and objectives: accurate detection of type and severity of hepatitis is crucial for effective treatment of the disease. while several computational algorithms for detection of hepatitis have been proposed to date, their limited performance leaves room for further improvement. this paper proposes a novel computational method for the diagnosis of hepatitis b using pattern detection tec...

The weighting exponentm is called the fuzzifier that can influence the performance of fuzzy c-means (FCM). It is generally suggested that mA[1.5,2.5]. On the basis of a robust analysis of FCM, a new guideline for selecting the parameter m is proposed. We will show that a large m value will make FCM more robust to noise and outliers. However, considerably large m values that are greater than the...

The fuzzy c-means (FCM) algorithm is a popular fuzzy clustering method. It is known that an appropriate assignment to feature weights can improve the performance of FCM. In this paper, we use the bootstrap method proposed by Efron [Efron, B., 1979. Bootstrap methods: Another look at the jackknife. Ann. Statist. 7, 1–26] to select feature weights based on statistical variations in the data. It i...