Functional connectivity, a flourishing new area of research in human neuroscience, carries a substantial challenge for visualization: while the end points of connectivity are known, the precise path between them is not. Although a large body of work already exists on the visualization of anatomical connectivity, the functional counterpart lacks similar development. To optimize the clarity of whole-brain and complex connectivity patterns in three-dimensional brain space, we develop mean-shift edge bundling, which reveals the multitude of connections as derived from correlations in the brain activity of cortical regions.

The understanding of brain networks becomes increasingly the focus of current research. In the context of functional magnetic resonance imagery (fMRI) data of the human brain, networks have been mostly detected using standard clustering approaches. In this work, we present a new method of detecting functional networks using fMRI data. The novelty of this method is that these networks have the property that every network member is closely connected with every other member. This definition might to be better suited to model important aspects of brain activity than standard cluster definitions. The algorithm that we present here is based on a concept from theoretical biology called "replicator dynamics."

This paper describes automatic procedures for extracting sulcal and gyral patterns from magnetic resonance (MR) images of the human brain. Specifically, we present three algorithms for the extraction of gyri, sulci, and sulcal fundi. These algorithms yield highly condensed line representations which can be used to describe the individual properties of the neocortical surface. The algorithms consist of a sequence of image analysis steps applied directly to the volumetric image data without requiring intermediate data representations such as surfaces or three-dimensional renderings. Previous studies have mostly focused on the extraction of surface representations, rather than line representations of cortical structures. We believe that line representations provide a valuable alternative to surface representations.

This paper describes a new approach to the automatic detection of the bottom lines of the main cortical sulci using MR images of the human brain. The principle idea is to extract lines of maximal depth as measured from the smoothed brain surface. The main advantage of our approach over existing methods is that it is not based on curvature estimation. It is therefore much more robust and easier to implement.

In the first part of this paper, an algorithm for synthesizing textures obeying a given set of co-occurrence features is presented. It is shown that the synthetically generated images are visually very similar to remotely sensed images coming from the Landsat/TM and ERS1/AMI sensors. This result motivates the use of those features for texture classification. In the second part of the paper, a new algorithm for classifying textures based on co-occurrence feature vectors that are modelled as multi-nomial density functions is presented.

This paper describes a system that assists in the planning and the performing of a surgical action, namely the corrective osteotomy of the thigh bone (femur). The corrective osteotomy of the thigh bone is used for changing the posture of the hip joint. During surgery, a small cross-sectional slice is cut out of the thigh bone by the use of a sawing device. Our system supports the planning phase of this surgery by allowing interactive visualizations of the cutting planes using a 3D surface-oriented model of the thigh bone derived from computer tomography (CT). It also supports the actual surgery, in which a robot hand is steered towards the predetermined cutting planes, a process that is guided and monitored by a vision system.>