Abstract. We propose a semi-supervised, kinetic modeling based segmentation technique for molecular imaging applications. It is an iterative, self-learning algorithm based on uncer...
Ahmed Saad, Benjamin Smith 0002, Ghassan Hamarneh,...
Abstract. We develop a segmentation technique for dynamic PET incorporating the physiological parameters for different regions via kinetic modeling. We demonstrate the usefulness o...
Ahmed Saad, Benjamin Smith 0002, Ghassan Hamarneh,...
We describe an algorithm for context-based segmentation of visual data. New frames in an image sequence (video) are segmented based on the prior segmentation of earlier frames in ...
A new probabilistic image segmentation model based on hypothesis testing and Gibbs Random Fields is introduced. First, a probabilistic difference measure derived from a set of hyp...
Abstract. We propose a new generative model, and a new image similarity kernel based on a linked hierarchy of probabilistic segmentations. The model is used to efficiently segment ...