Multiresolutional graph cuts for brain extraction from MR images

Yong-Sheng Chen*, Li Fen Chen, Yi Ting Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationFifth International Conference on Digital Image Processing, ICDIP 2013
DOIs
StatePublished - 13 Dec 2013
Event5th International Conference on Digital Image Processing, ICDIP 2013 - Beijing, China
Duration: 21 Apr 201322 Apr 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8878
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference5th International Conference on Digital Image Processing, ICDIP 2013
CountryChina
CityBeijing
Period21/04/1322/04/13

Keywords

  • brain extraction
  • graph cuts
  • MRI

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