PhD Proposal: Visualization of Volumetric Data in Immersive Virtual Environments

Recent advances in imaging technology have made available large-scale high-resolution data which is changing the way science and medicine are practiced. Fine details in high-resolution data enable researchers to answer questions they could not before. Whereas computers can outperform humans in many aspects, human vision is extraordinary in identifying trends, patterns, and anomalies in complex data. We are interested in visual abstraction, summarization, and depiction that can leverage the complementary strengths of humans and computers to reduce the time to discovery in biomedicine.
Our preliminary work shows the power of visualization in understanding complex interactions in rule-based reaction network models and microstructure textures in large gigapixel electron microscopy images. My proposed research is an extension of the current work to the visualization of higher-dimensional data with biomedical applications.
Our work on reaction network visualization creates a compact global summary of the relationships among participating entities and presents rule-dependent details upon user request. Through interaction the visualization avoids overwhelming users with too much information at once.
The visual encoding keeps each rule in the context of the whole network to facilitate the understanding and verification of the network model. Our work on recolorization of gigapixel images facilitates comprehension by mapping texture similarity to color spaces for ease in visual understanding. By highlighting regions of different textures with colors, users can easily recognize, even in low resolution, where the texturally-distinct regions are. Our system also allows the users to interactively traverse a hierarchy of image segments guided by a joint histogram of intensity and local-ternary patterns.
Extending techniques from our preliminary work, we will develop a visual analytic tool in an immersive virtual environment to assist the analysis of volumetric data. We will address the following two challenges: 1) improving the understanding of complex structures when viewing stereoscopic rendering of volumetric data in an immersive virtual environment, and 2) designing a volume visualization that, based on task-dependent criteria, facilitates rendering and quantitative analysis of the features of interest.
Examining Committee:
Committee Chair: - Dr. Amitabh Varshney
Dept's Representative - Dr. Dana Nau
Committee Member(s): - Dr. Joseph Jaja
- Dr. Antonio Cardone