Supervised Manifold Distance Segmentation

Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms

Visual exploration of volumetric datasets to discover the embedded features and spatial structures is a challenging and tedious task. In this paper we present a semi-automatic approach to this problem that works by visually segmenting the intensity-gradient 2D histogram of a volumetric dataset into an exploration hierarchy. Our approach mimics user exploration behavior by analyzing the histogram with the normalized-cut multilevel segmentation technique. Unlike previous work in this area, our technique segments the histogram into a reasonable set of intuitive components that are mutually exclusive and collectively exhaustive. We use information-theoretic measures of the volumetric data segments to guide the exploration. This provides a data-driven coarse-to-fine hierarchy for a user to interactively navigate the volume in a meaningful manner.

Interactive Extraction of Neural Structures with User-Guided Morphological Diffusion

Extracting neural structures with their fine details from confocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ine ective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.