Interactive combining of functional images

Feature extraction techniques for exploratory visualization of vector-valued imagery

This paper addresses the exploratory visualization of multispectral image data. In such data, each component of the vector pixel corresponds to a different imaging modality or a different combination of imaging parameters, and may provide different levels of contrast sensitivity between different regions of the underlying image. We address the problem of presenting this multidimensional data to human observers by synthesizing a display matched to their visual capabilities. Specifically, we seek to determine a data-adaptive linear projection of the vector data to one dimension that produces a grayscale image providing maximum discrimination between the different regions of the underlying object. The approach is equivalent to the extraction of the best linear feature of the vector field. Several new feature-extraction criteria that take into account both the spatial and multivariate structures of the data are proposed and illustrated by simulations on test images.

Unified multiple-feature color display for MR images

A display method is proposed in which the spin-lattice relaxation time T1, the spin-spin relaxation time T2, and the proton density rho of each pixel in a MR image are simultaneously expressed in color features in a unified way that allows international standardization. MR images were made from a phantom, a healthy volunteer, and patients in such a way that T1 and T2 and proton density images could be derived. T1 and T2 data were compared with accurate relaxation time measurements of the phantom content. Color images were computed from the acquired T1 and T2 images using matrix multiplication on a pixel base. In this way the color combination in each pixel represents the properties of that particular pixel by a unique mixing of the elementary colors red, green, and blue. Color resolution could be modified using different choices of the reference triangle in which the color combinations were defined. This method of representation offers a means for displaying multiple features as T1 and T2 in one directly interpretable image, independent of instrumental settings.