Atlas-based segmentation has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In this paper, we propose a method to exploit both the robust...
We address the difficulty of image segmentation methods based on the popular level set framework to handle an arbitrary number of regions. While in the literature some level set t...
This paper presents a novel level set approach to simultaneous tissue segmentation and bias correction of Magnetic Resonance Imaging (MRI) images. We first model the distribution ...
Fast level set methods replace continuous PDEs by a discrete formulation, improving the execution times. The regularization in fast level set methods was so far handled indirectly...
We present a new variational level-set-based segmentation
formulation that uses both shape and intensity prior information
learned from a training set. By applying Bayes’
rule...