Markovian method for 2D, 3D and 4D segmentation of MRI

Magnetic resonance imaging (MRI) is well adapted for early detection of diseases such as aortic aneuryms or dissections. In this paper, we present a new Markovian method which evolves an active contour for 2D, 3D and 4D (3D + time) segmentation. As opposed to other Markovian contour-based methods, our approach considers an implicit contour as the boundary of a 2D region. The regions are modeled via a Markov random field (MRF) and their computation is based on the maximum a posteriori probability criterion solved using an ICM algorithm. Our method depends on only one parameter that controls region boundary smoothness, is fast, easy to implement and can accommodate different likelihood functions to handle images with very different characteristics. Results on real and synthetic MRI are presented.