title: Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review.
abstract: Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.
authors.affiliation: Jussi Tohka, Department of Signal Processing, Tampere University of Technology, FIN-33101 Tampere, Finland.
authors.forename: Jussi
authors.initials: J
authors.lastname: Tohka
keywords: Brain imaging; Gray matter; Image analysis; Image processing; Magnetic resonance imaging; Segmentation; Tissue classification; White matter