17,027 research outputs found
Mapping Topographic Structure in White Matter Pathways with Level Set Trees
Fiber tractography on diffusion imaging data offers rich potential for
describing white matter pathways in the human brain, but characterizing the
spatial organization in these large and complex data sets remains a challenge.
We show that level set trees---which provide a concise representation of the
hierarchical mode structure of probability density functions---offer a
statistically-principled framework for visualizing and analyzing topography in
fiber streamlines. Using diffusion spectrum imaging data collected on
neurologically healthy controls (N=30), we mapped white matter pathways from
the cortex into the striatum using a deterministic tractography algorithm that
estimates fiber bundles as dimensionless streamlines. Level set trees were used
for interactive exploration of patterns in the endpoint distributions of the
mapped fiber tracks and an efficient segmentation of the tracks that has
empirical accuracy comparable to standard nonparametric clustering methods. We
show that level set trees can also be generalized to model pseudo-density
functions in order to analyze a broader array of data types, including entire
fiber streamlines. Finally, resampling methods show the reliability of the
level set tree as a descriptive measure of topographic structure, illustrating
its potential as a statistical descriptor in brain imaging analysis. These
results highlight the broad applicability of level set trees for visualizing
and analyzing high-dimensional data like fiber tractography output
Veni Vidi Vici, A Three-Phase Scenario For Parameter Space Analysis in Image Analysis and Visualization
Automatic analysis of the enormous sets of images is a critical task in life
sciences. This faces many challenges such as: algorithms are highly
parameterized, significant human input is intertwined, and lacking a standard
meta-visualization approach. This paper proposes an alternative iterative
approach for optimizing input parameters, saving time by minimizing the user
involvement, and allowing for understanding the workflow of algorithms and
discovering new ones. The main focus is on developing an interactive
visualization technique that enables users to analyze the relationships between
sampled input parameters and corresponding output. This technique is
implemented as a prototype called Veni Vidi Vici, or "I came, I saw, I
conquered." This strategy is inspired by the mathematical formulas of numbering
computable functions and is developed atop ImageJ, a scientific image
processing program. A case study is presented to investigate the proposed
framework. Finally, the paper explores some potential future issues in the
application of the proposed approach in parameter space analysis in
visualization
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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