title: Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models.
abstract: In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images. The proposed approach is primarily based on the integration of a stochastic model [a two-level Markov-Gibbs random field (MGRF)] that serves to learn the visual appearance of the brain texture, and a geometric model (the brain isosurfaces) that preserves the brain geometry during the extraction process. The proposed framework consists of three main steps: 1) Following bias correction of the brain, a new three-dimensional (3-D) MGRF having a 26-pairwise interaction model is applied to enhance the homogeneity of MR images and preserve the 3-D edges between different brain tissues. 2) The nonbrain tissue found in the MR images is initially removed using the brain extraction tool (BET), and then the brain is parceled to nested isosurfaces using a fast marching level set method. 3) Finally, a classification step is applied in order to accurately remove the remaining parts of the skull without distorting the brain geometry. The classification of each voxel found on the isosurfaces is made based on the first- and second-order visual appearance features. The first-order visual appearance is estimated using a linear combination of discrete Gaussians (LCDG) to model the intensity distribution of the brain signals. The second-order visual appearance is constructed using an MGRF model with analytically estimated parameters. The fusion of the LCDG and MGRF, along with their analytical estimation, allows the approach to be fast and accurate for use in clinical applications. The proposed approach was tested on in vivo data using 300 infant 3-D MR brain scans, which were qualitatively validated by an MR expert. In addition, it was quantitatively validated using 30 datasets based on three metrics: the Dice coefficient, the 95% modified Hausdorff distance, and absolute brain volume difference. Results showed the capability of the proposed approach, outperforming four widely used BETs: BET, BET2, brain surface extractor, and infant brain extraction and analysis toolbox. Experiments conducted also proved that the proposed framework can be generalized to adult brain extraction as well.
authors.forename: Amir
authors.initials: A
authors.lastname: Alansary
authors.forename: Marwa
authors.initials: M
authors.lastname: Ismail
authors.forename: Ahmed
authors.initials: A
authors.lastname: Soliman
authors.forename: Fahmi
authors.initials: F
authors.lastname: Khalifa
authors.forename: Matthew
authors.initials: M
authors.lastname: Nitzken
authors.forename: Ahmed
authors.initials: A
authors.lastname: Elnakib
authors.forename: Mahmoud
authors.initials: M
authors.lastname: Mostapha
authors.forename: Austin
authors.initials: A
authors.lastname: Black
authors.forename: Katie
authors.initials: K
authors.lastname: Stinebruner
authors.forename: Manuel F
authors.initials: MF
authors.lastname: Casanova
authors.forename: Jacek M
authors.initials: JM
authors.lastname: Zurada
authors.forename: Ayman
authors.initials: A
authors.lastname: El-Baz
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007223:Infant; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical