Unsupervised partial volume estimation using 3D and statistical priors
Pierre-Martin Tardif · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
Our main objective is to compute the volume of interest of images from magnetic resonance imaging (MRI). We suggest a method based on maximum a posteriori. Using texture models, we propose a new partial volume determination. We model tissues using generalized gaussian distributions fitted from a mixture of their gray levels and texture information. Texture information relies on estimation errors from multiresolution and multispectral autoregressive models. A uniform distribution solves large estimation errors, when dealing with unknown tissues. An initial segmentation, needed by the multiresolution segmentation deterministic relaxation algorithm, is found using an anatomical atlas. To model the a priori information, we use a full 3-D extension of Markov random fields. Our 3-D extension is straightforward, easily implemented, and includes single label probability. Using initial segmentation map and initial tissues models, iterative updates are made on the segmentation map and tissue models. Updating tissue models remove field inhomogeneities. Partial volumes are computed from final segmentation map and tissue models. Preliminary results are encouraging.