A framework for visually querying a probabilistic model of tumor image features.
Imaging plays an important role in characterizing tumors. Knowledge inferred from imaging data has the potential to improve disease management dramatically, but physicians lack a tool to easily interpret and manipulate the data. A probabilistic disease model, such as a Bayesian belief network, may be used to quantitatively model relationships found in the data. In this paper, a framework is presented that enables visual querying of an underlying disease model via a query-by-example paradigm. Users draw graphical metaphors to visually represent features in their query. The structure and parameters specified within the model guide the user through query formulation by determining when a user may draw a particular metaphor. Spatial and geometrical features are automatically extracted from the query diagram and used to instantiate the probabilistic model in order to answer the query. An implementation is described in the context of managing patients with brain tumors.
Hsu
William
W
Medical Imaging Informatics Group, University of California, Los Angeles, CA, USA.
Bui
Alex A T
AA
D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003952:Diagnostic Imaging; D006801:Humans; D015233:Models, Statistical