Analysis and Visualization Techniques for Dynamic Emission Tomography Images

We propose novel analysis and visualization techniques for dynamic emission tomography images. First, we propose a semi-supervised, kinetic-modeling-based segmentation technique to identify functional regions of interest. It is an iterative, self-learning algorithm based on uncertainty principles and is designed to be robust to the problems of low signal-to-noise ratio and partial volume effect. Second, we develop an interactive analysis and visualization tool for probabilistic segmentations of medical images. We provide a systematic approach to analyze, interact, and highlight regions of segmentation uncertainty. Finally, we introduce a set of multidimensional transfer function widgets to analyze multivariate probabilistic field data. These widgets furnish the user with contextual information about conformance or deviation from the population statistics. We demonstrate the ability to identify suspicious regions (e.g. tumors) and to correct misclassification results.