Elsevier

NeuroImage

Volume 22, Issue 3, July 2004, Pages 1060-1075
NeuroImage

A hybrid approach to the skull stripping problem in MRI

https://doi.org/10.1016/j.neuroimage.2004.03.032Get rights and content

Abstract

We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools.

Introduction

Whole-brain segmentation, called skull stripping, is an important technique for the analysis of neuroimaging data. Many applications, such as presurgical planning, cortical surface reconstruction and brain morphometry, depend on the ability to accurately segment brain from nonbrain tissue, e.g., remove extracerebral tissue such as skull, eyeballs, and skin. In addition, these techniques allow the construction of detailed head models that can be used to fuse MRI data with EEG and MEG sensor information to generate spatiotemporal maps of brain activity Dale and Sereno, 1993, Faugeras et al., 1999.

Current automatic approaches to automated skull stripping can be roughly divided into three categories: region-based, boundary-based, and hybrid approaches.

  • Region-based methods identify connected regions based on predefined criteria (typically intensity), employing thresholding, clustering, and morphological filtering to identify the targeted volume. While some published approaches are effective, region-based methods generally involve some degree of user interaction, and are sensitive to scanning parameters and intensity inhomogeneity. For example, Atkins and Mackiewich (1998) use thresholding and morphology techniques, combined with an anisotropic diffusion process to localize and segment the brain. Meegama et al. (2001) propose a similar approach. The method proposed by Ward Cox, 1996, Ward, 1999 generates a segmented brain volume by assembling segmented slices. Morphological operations are used to smooth the brain envelope and refine the final segmentation. Another example can be found in Lemieux et al. (1999). Watershed techniques constitute a special case of region-based methods. The gradient intensity is usually the criterion defining connectivity, but some intensity-based approaches are used in a similar way, with the advantage of being less noise-sensitive. One of the main drawbacks of these methods is that they suffer from oversegmentation, which is the reason why they are usually followed by a postprocessing step to merge separate regions that belong to the same structure. Hahn and Peitgen (2000) proposed a solely intensity-based watershed algorithm, which makes use of a simple merging criterion to avoid the oversegmentation problem. In contrast to most region-based methods, their technique is particularly well adapted to brain segmentation, and is quite robust to intensity inhomogeneities.

  • Boundary-based methods primarily rely on gradient information to locate the brain surface, usually modeled by an active contour. For instance, template-based methods incorporate shape information into the segmentation process, iteratively matching a balloon-like template to the brain surface, using image-based and smoothing forces Dale et al., 1999, Kapur et al., 1995, Smith, 2002. Compared to region-based methods, these approaches seem more robust and less sensitive to image artifacts, and require less user-interaction. On the other hand, their success often depends on the quality of initialization and manual adjustment to scanning parameters. Furthermore, boundary-based segmentation produces recurrent errors in some part of the brain, such as the base of the cerebellum and temporal poles.

  • Hybrid approaches combine the two previous methods. Kapur et al. (1995) propose a hybrid approach that uses morphological operations and active contour segmentation. Their method requires a preprocessing step, “Adaptative Segmentation” by Wells et al. (1996), which corrects for the gain introduced in the data by the imaging equipment. Shattuck and Leahy (in press), based on Shattuck et al. (2001), use adaptive anisotropic diffusion, edge detection and morphological erosions to identify the brain component. More recently, new hybrid approaches have been proposed to accurately locate the inner and outer surfaces of the brain, even in the depths of sulci. For this purpose, level-set methods are becoming of great use, representing the targeted evolving surface by the zero level set of a three-dimensional function. For instance, Xu et al. (1999) deform the active surface under a gradient vector field computed from a binary edge map. Motivated by the nearly constant thickness of the cortex, Zeng et al. (1999) use a coupled surface evolution to extract bounding surfaces of the cortex. Another method is proposed by Dawant et al. (1999), in which a combination of global similarity transforms and local free-form deformations to delineate internal structures of the brain. Other methods can be found in Atkins and Mackiewich (1998), Baillard et al. (2001), Fischl et al. (2002), Goldenberg et al. (2001), MacDonald et al. (2000), Pham and Prince (1999), Rajapakse (1997), Rehm et al. (1999) and Zhang et al. (2001)

Nevertheless, due to the presence of imaging artifacts, anatomical variability, varying contrast properties, and poor registration, most of these techniques do not give satisfactory results over a wide range of scan types and neuroanatomies without manual intervention. In this paper, we propose a hybrid approach to robustly and automatically segment brain from non-brain in T1-weighted MR images. We note that the algorithm does not aim at extracting the brain surface in its deepest folds; some techniques for doing so as a post-processing step can be found in Atkins and Mackiewich (1998), Dale et al. (1999), Xu et al. (1999) and Zeng (1999). Our method combines the robustness to noise that makes watershed approaches attractive, with the geometric information that are available to deformable surface algorithms. Appropriate values for the parameters of the algorithm are automatically computed during the processing. A comparison with existing techniques is reported in the final section.

Section snippets

Methods

Regarding brain anatomy, our approach relies on a few general assumptions:

  • Similar to other approaches, our first basic assumption is the connectivity of the white matter. The white matter (WM) constitutes a connected region that is bordered by gray matter (GM) and cerebrospinal fluid (CSF). In T1-weighted MR images, WM voxels have bright intensities and are surrounded by darker GM voxels and even darker CSF voxels.

  • The brain surface, which separates brain from nonbrain regions, is a smooth

Assessment of the results and discussion

Stripping the skull and other nonbrain tissues from the structural images of the head is a challenging and critical component for a variety of post-processing tasks. Large anatomical variability among brains, different acquisition methods, and the presence of artifacts increase the difficulty of designing a robust algorithm, thus current techniques are often susceptible to problems and require manual intervention. To validate the proposed algorithm, we compared it to four existing techniques,

Conclusion

Our goal, when implementing this new skull-stripping algorithm, was to develop an automated algorithm able to successfully segment the whole brain, without any user intervention. This new segmentation process, based on a hybrid approach, which combines watershed algorithms and deformable surface models, offers the user both the robustness of watershed algorithms and the accuracy of deformable surface models. Surface-based methods, which easily incorporate geometric information, do not have

Acknowledgements

This work was supported in part by the National Center for Research Resources (P41-RR14075, R01 RR16594-01A1), the NCRR BIRN Morphometric Project BIRN002), as well as the Mental Illness and Neuroscience Discovery (MIND) Institute. The authors would like to thank Dr. Olivier Faugeras (Project Robotvis-I.N.R.I.A) for his help and contribution to this research, Randy Buckner and the Washington University Alzheimer's Disease Research Center for providing data and the anonymous reviewers for their

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