Faculty: Shiaofen Fang : Research Projects

Research Projects

Shiaofen Fang



Visual Analytics of Neuroimaging Data

This is a collaborative project with the Department of Radiology and Imaging Sciences of Indiana University. We aim to develop human brain image analysis and visualization techniques and tools for the visual exploration and analysis of human brain image data. Pattern recognition techniques are applied to detect imaging biomarkers for various conditions. Visualization techniques are developed to visualize features of the brain connectome network including topology, attributes, clusters, markers, genetic associations, and their correlations, within the context of volumetric anatomical features. Visual analytics techniques are also being developed for the analysis tasks such as diagnostic biomarker detection. This project is currently funded by NIH-NIBIB and by IUPUI Imaging Technology Development Program (ITDP) .


Healthcare Data Visualization

This is a collaboration with researchers in the Regenstrief Institute and the School of Informatics to develop new visualization techniques and an interactive visualization system for large healthcare data sets. Such a system offers a real time and web-based solution for the effective use of large scale electronic health record systems by allowing system level integration of the human visual capabilities into the overall health data based decision making system. We developed a novel concept space approach to compress large, heterogeneous, and historical patient and public health data into a single, intuitive and comprehensive visualization. New spatiotemporal visualization techniques were developed for large public health datasets that involve geographical and population wide information. This project has been funded by the US Department of Defense (US Army).


Information Visualization Algorithms

We are interested in developing various general purpose information visualization algorithms. Some examples include (1) Gene Terrain, a large scale graph visualization technique based on scattered data interpolation; (2) Spiral Theme Plot, a time-series data visualization technique; and (3) Color Time Curves, a spatiotemporal data visualization technique. These techniques have been applied to various data analytics and visualization applications such as disease biomarker detection using disease networks and protein-protein interaction network; healthcare data visualization, city traffic data visualization, and text visualization for online review data and unstructured text data.


3D Facial Image Analysis for FASD Diagnosis

This was a collaboration with NIH Collaborative Initiative on Fetal Alcohol Spectrum Disorders (CIFASD). We have developed 3D image analysis techniques for Fetal Alcohol Syndrome diagnosis. The focus is on enhancing our understanding of FASD dysmorphology through the processing and analysis of 3D facial images. We have also developed mouse models for facial and brain phenotypes as a function of the dose and stage of embryonic development of the alcohol exposure. New applications of 3D Micro-video-imaging and Micro-computed tomography (Micro-CT) imaging of facial and underlying bone/cartilage allow high resolution analysis of surface-to-bone/cartilage craniofacial dysmorphology from fetal ages to young adulthood. This project was funded by multiple NIH-NIAAA grants.


Volume Graphics

My earlier research has been focused on volume rendering algorithms and volume graphics techniques for interactive volumetric modeling systems. I have developed several algorithms for deformable volume rendering and transfer function design in volume visualization. I have also developed a framework of hardware assisted techniques and voxelization algorithms that would allow 3D modeling operations to be carried out interactively in a volume graphics environment. Our results demonstrate that it is possible to achieve high performance volume graphics and volume modeling with the architecture of existing graphics subsystems without any special hardware design. This project was funded by an NSF grant.


Last Modified: Dec 16, 2016