Level set approach for brain tissue modeling

Brain atlas based techniques have proven to be a successful way to map  individual patient magnetic resonance brain volumes, by exploiting a priori knowledge of the "average" human brain. However, the variance of individual brain regions have proven so prohibitive that manual interaction is required to segment the and classify brain tissue as either white matter, grey matter, or cerebrospinal fluid. Bourouis et al (2008) propose a technique to do so automatically.

The first step is to correlate each voxel in the atlas space with one in the patient's brain volume data, and then calculate the posterior probability that a voxel is assigned to a tissue class based on the intensity of the MRI data. After the algorithm converges, each voxel has been defined by the tissue class with the max posterior probability. Their model then takes into account geometric properties of the data such as the curvature and smoothness of the region, based on the level set approach. Results would still be validated by experts, but such an algorithm would help to automate a necessary but relatively time consuming step in identifying brain tumors.

Reference

Bourouis S, Jamrouni K, Betrouni N. 2008 Lecture notes in computer science.: Automatic MRI brain segmentation with combined atlas-based classification and level-set approach. In ICIAR 2008, LNCS 5112, pp. 770-778.