Skull-stripping magnetic resonance brain images using a model-based level set

Abstract

The segmentation of brain tissue from nonbrain tissue in magnetic resonance (MR) images, commonly referred to as skull-stripping, is an important image processing step in many neuroimaging studies. A new mathematical algorithm, a model-based level set (MLS), was developed for controlling the evolution of the zero level curve that is implicitly embedded in the level set function. The evolution of the curve was controlled using two terms in the level set equation, whose values represented the forces that determined the speed of the evolving curve. The first force was derived from the mean curvature of the curve, and the second was designed to model the intensity characteristics of the cortex in MR images. The combination of these forces in a level set framework pushed or pulled the curve toward the brain surface. Quantitative evaluation of the MLS algorithm was performed by comparing the results of the MLS algorithm to those obtained using expert segmentation in 29 sets of pediatric brain MR images and 20 sets of young adult MR images. Another 48 sets of elderly adult MR images were used for qualitatively evaluating the algorithm. The MLS algorithm was also compared to two existing methods, the brain extraction tool (BET) and the brain surface extractor (BSE), using the data from the Internet brain segmentation repository (IBSR). The MLS algorithm provides robust skull-stripping results, making it a promising tool for use in large, multi-institutional, population-based neuroimaging studies.

Methods on Skull Stripping of MRI Head Scan Images—a Review

“…As a final step, it performs overlap tests on candidates brain regions of interest in the neighboring slice images to propagate coherent 2D brain masks through the third dimension. However, existing methods that use mathematical morphology are sometimes sensitive to small data variations and it is difficult to find the optimum morphology size for separating the brain tissues from the non-brain tissues [31,32]. A similar method proposed by Tsai et al [33] is based on histogram analysis and morphological operations.…”

“…Model-based level set method (MLS) by Zhuang et al [32] is based on active curve to remove the skull and intracranial tissues surrounding the brain in MR brain images. It was developed for controlling the evolution of the zero level curve that is implicitly embedded in the level set function.…”

BEaST: Brain extraction based on nonlocal segmentation technique

“…The number of methods proposed to address the brain segmentation problem reflects the importance of accurate and robust brain extraction. During the last 15 years, more than 20 brain extraction methods have been proposed using a variety of techniques, such as morphological operations (Goldszal et al, 1998;Lemieux et al, 1999;Mikheev et al, 2008;Park and Lee, 2009;Sandor and Leahy, 1997;Ward, 1999), atlas matching (Ashburner and Friston, 2000;Kapur et al, 1996), deformable surfaces (Dale et al, 1999;Smith, 2002), level sets (Baillard et al, 2001;Zhuang et al, 2006), histogram analysis (Shan et al, 2002), watershed (Hahn andPeitgen, 2000), graph cuts (Sadananthan et al, 2010), label fusion (Leung et al, 2011), and hybrid techniques (Carass et al, 2011;Iglesias et al, 2011;Rehm et al, 2004;Rex et al, 2004;Segonne et al, 2004;Shattuck et al, 2001). Studies evaluating these methods have found varying accuracy (Boesen et al, 2004;Fennema-Notestine et al, 2006;Hartley et al, 2006;Lee et al, 2003;Park and Lee, 2009;Shattuck et al, 2009).…”

Level-Set-Segmentierung von Rattenhirn MRTs

“…Im weiteren Verlauf wird die Geschwindigkeitsfunktion S( x) noch eine zentrale Rolle einnehmen und genau wie in [Zhu06] und [Pal08] zusätzlich in zeitlicher Richtung variiert werden.…”