Image Analysis


The University of Utah School of Computing’s research in image analysis addresses fundamental questions in 2D and 3D images and applications in a variety of fields including energy, defense, biology, and medicine. Challenging applications drive the development of new methods for image processing, shape representation and analysis, and computational statistics. This research spans a wide range of areas including new methods for low-level image processing, such as filtering, segmentation, and reconstruction of objects of interest. Work in image analysis also focuses on computational anatomy tools for building population atlases, representation and modeling of shapes, and on statistical methods for analyzing populations of image and of shapes, with applications of these methods to problems in medicine and biology.

A particularly important application area is neuroimage analysis, and collaborations with the University of Utah Brain Institute and other national partners focus on disorders and diseases such as autism and Alzheimers. Examples are the analysis of diffusion-weighted MRI images for quantifying brain connectivity, the analysis of longitudinal pediatric images to understand early brain development, and analysis of functional data for understanding brain function and the effects of disease. Another focus area is analysis of large-scale microscopy data in a multi-disciplinary research effort with biology and medicine. The research and development work also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and real-time implementations on graphics processing units (GPUs).

We further participate in national efforts for the development of open-source image analysis software to be integrated into freely distributed toolkits. More information on ongoing research activities related to imaging is found at UCNIA and the SCI Institute.

Research Activities

  • Image segmentation and analysis
  • Statistical analysis of multimodal image data
  • Pattern recognition and machine learning
  • Pair-wise and group-wise image registration
  • Computational anatomy
  • Shape representation and analysis
  • Modeling of pathological and growth processes
  • Analysis of diffusion weighted imaging
  • Analysis spatiotemporal and longitudinal image data