Paper Info
Title
Multiresolutional graph cuts for brain extraction from MR images
Abstract
This paper presents a multiresolutional brain extraction framework which utilizes graph cuts technique to classify head magnetic resonance (MR) images into brain and non-brain regions. Starting with an over-extracted brain region, we refine the segmentation result by trimming non-brain regions in a coarse-to-fine manner. The extracted brain at the coarser level will be propagated to the finer level to estimate foreground/background seeds as constraints. The short-cut problem of graph cuts is reduced by the proposed pre-determined foreground from the coarser level. In order to consider the impact of the intensity inhomogeneities, we estimate the intensity distribution locally by partitioning volume images of each resolution into different numbers of smaller cubes. The graph cuts method is individually applied for each cube. Compared with four existing methods, the proposed method performs well in terms of sensitivity and specificity in our experiments for performance evaluation.
Year
DOI
Venue
2013
10.1117/12.2031540
Proceedings of SPIE
Keywords
Field
DocType
MRI,brain extraction,graph cuts
Cut,Computer vision,Computer science,Segmentation,Artificial intelligence,Trimming,Cube
Conference
Volume
ISSN
Citations 
8878
0277-786X
0
PageRank 
References 
Authors
0.34
5
3
Name
Order
Citations
PageRank
Yong-Sheng Chen131430.12
Li-Fen Chen2183.31
yiting wang300.34