David H. Laidlaw

TL;DR: Expert feedback from doctors studying changes in white-matter structures after gamma-knife capsulotomy and preoperative planning for brain tumor surgery shows that streamtubes correlate well with major neural structures, the 2D section and geometric landmarks are important in understanding the visualization, and the stereo and interactivity from the virtual environment aid inUnderstanding the complex geometric models.

TL;DR: In this paper, a probabilistic Bayesian approach is used to identify the distribution of different material types in volumetric datasets such as those produced with Magnetic Resonance Imaging (MRI) or Computed Tomography (CT).

TL;DR: A new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT) is presented, which has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well.

TL;DR: A new visualization method for 2d flows which allows us to combine multiple data values in an image for simultaneous viewing and uses a combination of discrete and continuous visual elements arranged in multiple layers to visually represent the data.

TL;DR: This work describes an application of proper orthogonal decomposition (POD) for assigning importances to kinematic variables, using dimensional complexity as a metric, and uncovers three groups of joints that move together during flight by using POD to quantify correlations of motion.