Eigenimages and multivariate analyses

Functional Connectivity: Eigenimages and multivariate analyses

A multivariate analysis of PET activation studies.

In this paper we present a general multivariate approach to the analysis of functional imaging studies. This analysis uses standard multivariate techniques to make statistical inferences about activation effects and to describe the important features of these effects. More specifically, the proposed analysis uses multivariate analysis of covariance (ManCova) with Wilk's lambda to test for speci...

A Novel Color Image Compression Method Using Eigenimages

Since the birth of multi–spectral imaging techniques, there has been a tendency to consider and process this new type of data as a set of parallel gray–scale images, instead of an ensemble of an n–D realization. Although, even now, some researchers make the same assumption, it is proved that using vector geometries leads to better results. In this paper, first a method is prop...

Representing Image Matrices: Eigenimages Versus Eigenvectors

We consider the problem of representing image matrices with a set of basis functions. One common solution for that problem is to first transform the 2D image matrices into 1D image vectors and then to represent those 1D image vectors with eigenvectors, as done in classical principal component analysis. In this paper, we adopt a natural representation for the 2D image matrices using eigenimages,...

Representing Image Matrices: Eigenimages vs. Eigenvectors

We consider the problem of representing image matrices with a set of basis functions. One common solution for that problem is to first transform the 2D image matrices into 1D image vectors and then to represent those 1D image vectors with eigenvectors, as done in classical principal component analysis. In this paper, we adopt a natural representation for the 2D image matrices using eigenimages,...