The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means. However, it is still not clear which is ...

The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applyin...

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...

This paper proposes a new clustering algorithm which integrates Fuzzy C-means clustering with Markov random field (FCM). The density function of the first principal component which sufficiently reflects the class differences and is applied in determining of initial labels for FCM algorithm. Thus, the sensitivity to the random initial values can be avoided. Meanwhile, this algorithm takes into a...

Much research has shown that fuzzy c-means clustering is a powerful tool for partitioning samples into different categories. However, the cost function of the classical fuzzy c-means (FCM) is defined by the distances from data to the cluster centers with their fuzzy memberships. In this study, a new fuzzy clustering algorithm, namely the fuzzy weighted c-means (FWCM), is proposed. In this propo...

It is found that sub-pixel classifiers for classification of multi-spectral remote sensing data yield a higher accuracy. With this objective, a study has been carried out, where fuzzy set theory based sub-pixel classifiers have been compared with statistical based sub-pixel classifier for classification of multi-spectral remote sensing data.Although, a number of Fuzzy set theory based classifie...

Fuzzy clustering techniques, especially fuzzy c-means (FCM) clustering algorithm, have been widely used in automated image segmentation. The performance of the FCM algorithm depends on the selection of initial cluster center and/or the initial memberships value. if a good initial cluster center that is close to the actual final cluster center can be found. the FCM algorithm will converge very q...

In this paper, a fast and practical GPU-based implementation of Fuzzy C-Means (FCM) clustering algorithm for image segmentation is proposed. First, an extensive analysis is conducted to study the dependency among the image pixels in the algorithm for parallelization. The proposed GPU-based FCM has been tested on digital brain simulated dataset to segment white matter(WM), gray matter(GM) and ce...

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...