—We propose a unified graphical model that can represent both the causal and noncausal relationships among random variables and apply it to the image segmentation problem. Specif...
Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely perfor...
Kilian M. Pohl, John W. Fisher III, Ron Kikinis, W...
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...
This paper presents a novel method for image classification. It differs from previous approaches by computing image similarity based on region matching. Firstly, the images to be ...
This paper presents a novel segmentation approach featuring shape constraints of multiple structures. A framework is developed combining statistical shape modeling with a maximum a...
Kilian M. Pohl, Simon K. Warfield, Ron Kikinis, W....