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World J Radiol. Nov 28, 2014; 6(11): 855-864
Published online Nov 28, 2014. doi: 10.4329/wjr.v6.i11.855
Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review
Jussi Tohka
Jussi Tohka, Department of Signal Processing, Tampere University of Technology, FIN-33101 Tampere, Finland
Author contributions: Tohka J designed and wrote the article.
Correspondence to: Jussi Tohka, PhD, Department of Signal Processing, Tampere University of Technology, PO Box 553, FIN-33101 Tampere, Finland. jussi.tohka@tut.fi
Telephone: +358-40-1981497 Fax: +358-3-3641352
Received: June 23, 2014
Revised: September 3, 2014
Accepted: September 23, 2014
Published online: November 28, 2014
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.

Keywords: Magnetic resonance imaging, Segmentation, Tissue classification, White matter, Gray matter, Image processing, Brain imaging, Image analysis

Core tip: Each voxel in a brain magnetic resonance imaging (MRI) may contain multiple types of tissue. Partial volume estimation refers to a generalized image segmentation task where the amount of each tissue type within each image voxel of brain MRI is solved. This is important for volume quantification and cortical thickness analysis due to the geometrical complexity of human brain structure. This review aims to provide a systematic, tutorial-like overview of methods for partial volume estimation in brain MRI.