Medical image segmentation is an important tool in viewing and analyzing Magnetic Resonance Images (MRI) and solving variousranges of problems in medical imaging. This paper focuses the new approach to segmentation by clustering the image by Genetic Algorithm based Fuzzy C-means clustering (FCM). First segmentation can be done with the help of FCM. Fuzzy C-means can be used to segment the image...

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...

This paper considers the problem of partitioning noisy images into different regions by fuzzy clustering approach. Based on two fuzzy c-means (FCM) algorithms (FCM S1 and FCM S2), we propose four adaptive algorithms (FCM S11, FCM S12, FCM S21 and FCM S22) which utilize the high correlation of image pixels to increase the algorithms’ robustness to noise. Unlike existing algorithms, our algorithm...

C security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. The term “Intrusion” refers to any unauthorized access which attempts to compromise confidentiality, integrity and availability of information resources [1] [14] [32]. Traditional intrusion prevention techniques such as firewalls, access control and encryption...

The triple-frequency linear combination method can provide combinations with different characteristics and is one of the important methods to improve performance navigation services. Due large number performances, combinatorial clustering optimization very important, efficiency manual screening low. Firstly, based on basic model, objective equations are derived. Secondly, fuzzy c-means (FCM) al...

in this research, the framework is presented for unsupervised change detection using multitemporal sar images based on integration clustering and level set methods. spatial correlation between pixels were considered by using contextual information. also as proposed method was used integration of gustafson-kessel clustering techniques (gkc) and level set methods for change detection. using clust...

This paper, presents a new image segmentation method based on Wavelets, Particle Swarm Optimization (PSO) and outlier rejection caused by the membership function of the kernel fuzzy local information c-means (KFLICM) algorithm combined with level set is proposed. The segmentation of Magnetic Resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research, but...

The classification of the electrocardiogram (ECG) into different patho-physiological disease categories is a complex pattern recognition task. In this paper, we propose a scheme to integrate fuzzy c-means (FCM) clustering, principal component analysis (PCA) and neural networks (NN) for ECG beat classification. The PCA is used to decompose ECG signals into weighted sum of basic components that a...

Submitted: Aug 20, 2013; Accepted: Sep 28, 2013; Published: Oct 6, 2013 Abstract: Cluster analysis is used for clustering a data set into groups of similar individuals. It is an approach towards to unsupervised learning and is one of the major techniques in pattern recognition.FCM algorithm needs the number of classes and initial values of center for each cluster. These values are determined ra...

This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clust...