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) 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 SPM۸, FSL and Brainsuite is presented. Segmentation with SPM۸ has been performed in three frameworks: i) default segmentation, ii) SPM۸ New-segmentation and iii) modified version using hidden Markov random field as implemented in SPM۸-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.Comparison with Existing Method(s): 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.Conclusion: The obtained results can assist the users in choosing an appropriate segmentation software package for the neuroimaging application of interest.