Medical image segmentation with a 3D nearest neighbor Markov mesh

In the aim of tumor segmentation from magnetic resonance (MR) images, we employ a hidden 3D Markov mesh model that has been developed for 30 image segmentation in general and has shown promising results on synthetic image data. We model the signal intensity within the non-tumorous area in form of an equiprobable distribution, and we assume that the tumor is characterized by a Gaussian distribution. We introduce a class-specific weight coefficient to the Markov model, with which a clinical user can influence the segmentation result. The novelty of this contribution lies in the combination of a three-dimensional hidden mesh model with interaction possibilities for clinical use of the algorithm.