Automated cerebrum segmentation from three-dimensional sagittal brain MR images

Huh, Shin; Ketter, Terence A.; Sohn, Kwanghoon; Lee, Chulhee

doi:10.1016/s0010-4825(02)00023-9

Cited by 22 publications

(13 citation statements)

References 23 publications

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“…Poor results are obtained if the estimation and initialization are not done properly [31]. A connectivity-based threshold algorithm to extract the brain regions of 3D sagittal MR skull stripping was developed by [48].…”

Section: Intensity-based Methodsmentioning

confidence: 99%

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

2015

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The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

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Section: Intensity-based Methodsmentioning

confidence: 99%

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

2015

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“…Analysis of Functional NeuroImages (AFNI) fits a Gaussian mixture model to the intensity histogram of a brain image and estimates an intensity range to segment the brain areas in a sliceby-slice manner (Cox, 1996;Ward, 1999). Hahn and Peitgen (2000) presented a watershed algorithm which uses a connectivity criterion, pre-flooding height, to group image voxels with similar intensities and then regards the largest connected component as Brummer et al (1993), Lee et al (1998), Worth et al (1998), Hata et al (2000), Stokking et al (2000), and Huh et al (2002). Methods of this type are usually sensitive to image scanning parameters and image artifacts, such as noise and intensity inhomogeneity.…”

Section: Introductionmentioning

confidence: 99%

Accurate and robust extraction of brain regions using a deformable model based on radial basis functions

Liu

Chen

2009

Journal of Neuroscience Methods

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“…Lee et al (1998) proposed a 2D skull-stripping method applied to a midsagittal slice, which was later extended by Huh et al (2002) to all slices in a sagittal series. First, thresholds were used to separate dark pixels (e.g., background, skull and cavities, etc) from bright pixels (e.g., brain, skin, facial tissues, etc), then brain regions were identified using a connectivity-based algorithm.…”

Section: Introductionmentioning

confidence: 99%

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

2006

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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.

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Automated cerebrum segmentation from three-dimensional sagittal brain MR images

Cited by 22 publications

References 23 publications

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

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

Accurate and robust extraction of brain regions using a deformable model based on radial basis functions

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

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