Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation
Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) corre...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Shiraz University of Medical Sciences
2014-03-01
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| Series: | Journal of Biomedical Physics and Engineering |
| Subjects: | |
| Online Access: | http://www.jbpe.org/Journal_OJS/JBPE/index.php/jbpe/article/view/286 |
| Summary: | Background: Accurate brain tissue segmentation from magnetic resonance (MR)
images is an important step in analysis of cerebral images. There are software packages
which are used for brain segmentation. These packages usually contain a set of skull
stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus,
assessment of the quality of the segmented gray matter (GM), white matter (WM) and
cerebrospinal fluid (CSF) is needed for the neuroimaging applications.
Methods: In this paper, performance evaluation of three widely used brain segmentation
software packages SPM8, FSL and Brainsuite is presented. Segmentation with
SPM8 has been performed in three frameworks: i) default segmentation, ii) SPM8
New-segmentation and iii) modified version using hidden Markov random field as
implemented in SPM8-VBM toolbox.
Results: The accuracy of the segmented GM, WM and CSF and the robustness of
the tools against changes of image quality has been assessed using Brainweb simulated
MR images and IBSR real MR images. The calculated similarity between the segmented
tissues using different tools and corresponding ground truth shows variations
in segmentation results.
Conclusion: A few studies has investigated GM, WM and CSF segmentation. In
these studies, the skull stripping and bias correction are performed separately and they
just evaluated the segmentation. Thus, in this study, assessment of complete segmentation
framework consisting of pre-processing and segmentation of these packages is
performed. The obtained results can assist the users in choosing an appropriate segmentation
software package for the neuroimaging application of interest. |
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| ISSN: | 2251-7200 2251-7200 |