Gordon Kindlmann

TL;DR: An important class of 3D transfer functions for scalar data is demonstrated, and the application of multi-dimensional transfer functions to multivariate data is described, and a set of direct manipulation widgets that make specifying such transfer functions intuitive and convenient are presented.

TL;DR: It is demonstrated that for a large class of scalar volume data, namely that where the regions of interest are the boundaries between different materials, a transfer function which makes boundaries readily visible can be generated from the relationship between three quantities.

TL;DR: Findings from a comprehensive study of diffusion tensor indices and probabilistic tractography obtained in a very large population of healthy controls, MCI and probable AD subjects emphasise the benefit of looking at the more complex regions in which spared and affected pathways are crossing to detect very early alterations of the white matter that could not be detected in regions consisting of one fibre population only.

TL;DR: This paper demonstrates an important class of three-dimensional transfer functions for scalar data (based on data value, gradient magnitude, and a second directional derivative), and describes a set of direct manipulation widgets which make specifying such transfer functions intuitive and convenient.

TL;DR: The proposed methodology combines an implicit formulation of curvature with convolution-based reconstruction of the field, and gives concrete guidelines for implementing the methodology, and illustrates the importance of choosing accurate filters for computing derivatives with Convolution.