| Original language | English |
|---|---|
| Title of host publication | Brain Mapping: An Encyclopedic Reference : Volume 1: Acquisition Methods, Methods and Modeling |
| Volume | 1 |
| Publisher | Elsevier |
| Publication date | 2015 |
| Pages | 373-381 |
| ISBN (Print) | 978-0-12-397316-0 |
| DOIs | |
| Publication status | Published - 2015 |
Abstract
Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are now well established. In their simplest form, these methods classify voxels independently based on their intensity alone, although much more sophisticated models are typically used in practice.
This article aims to give an overview of often-used computational techniques for brain tissue classification. Although other methods exist, we concentrate on Bayesian modeling approaches, in which generative image models are constructed and subsequently ‘inverted’ to obtain automated segmentations. This general framework encompasses a large number of segmentation methods, including those implemented in widely used software packages such as SPM, FSL, and FreeSurfer.
This article aims to give an overview of often-used computational techniques for brain tissue classification. Although other methods exist, we concentrate on Bayesian modeling approaches, in which generative image models are constructed and subsequently ‘inverted’ to obtain automated segmentations. This general framework encompasses a large number of segmentation methods, including those implemented in widely used software packages such as SPM, FSL, and FreeSurfer.