Medical image segmentation with a 3D nearest neighbor Markov mesh

With the aim of tumor segmentation in magnetic resonance (MR) images, the authors employ a hidden 3D Markov mesh model that has been developed for 3D image segmentation in general and has shown promising results on synthetic image data. The authors model the signal intensity within the non-tumorous area in the form of an equiprobable distribution, and they assume that the tumor is characterized by a Gaussian distribution. The authors 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