Elements of uncertainty are inherent to management and analysis of complex image data for scientific and engineering applications. The work builds on previous multidisciplinary work for storage, management, and analysis of biological images of cellular architectures in the vertebrate central nervous system and sub-cellular environments, but the techniques are general and target other areas, such as environmental management, geographical information science, remote sensing and interactive digital multimedia. Imaging is at the cores of many scientific discoveries, with information captured in terms of raw pixel intensities and in multiple channels for color or hyperspectral imagery. The work includes generation of probabilistic measurements and quantified uncertainties from image analysis methods, pattern classification methods generating information that can be stored as probabilistic feature tables and new approaches to visualization of probabilistic information. The proposed work will be integrated within the UCSB BioImage Search and Query environment, part of the campus data infrastructure, and the software developed will be made available as open source.
