Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images

Lee, Chulhee; Huh, Shin; Ketter, Terence A.; Unser, Michaël

doi:10.1016/s0010-4825(98)00013-4

Cited by 62 publications

(37 citation statements)

References 23 publications

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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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Section: Introductionmentioning

confidence: 99%

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

2006

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“…Thresholding segmentation transforms input image, I, to a binary image, g, by grouping the pixels with intensities, higher than the threshold into one the class, and the further pixels into another class. For any 2D digital image [16], and ………………….. (2) The technique of thresholding is defined as:…”

Section: Thresholding Methodsmentioning

confidence: 99%

A Comparative Study on Classification of Image Segmentation Methods With a Focus on Graph Based Techniques

Basavaprasad¹

2014

IJRET

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“…In [3], a 2D-skull stripping method was proposed which was applied to a midsaggital slice. This method was later extended in [4] to all slices in a saggital series.…”

Section: Introductionmentioning

confidence: 99%

Accuracy based Comparison of Three Brain Extraction Algorithms

Mirajkar¹

2012

IJCA

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Skull stripping is an important image processing step in many neuroimaging studies. In this paper, a comparison of three brain extraction algorithms is done, namely Brain Surface Extractor (BSE), skull stripping algorithm using Geodesic Active Contour (GAC), and skull stripping using active contours without edges. The comparison is done with respect to accuracy of the three algorithms. The results provided by the three algorithms are compared against the processed results available in the OASIS dataset. A comparison of the three algorithms shows that BSE provides the best results with respect to the percentage of non-brain matter contained in the final segmented output. The algorithm using GAC produces a conservative result containing some amount of non-brain matter that can be removed using morphological operator. The algorithm using active contours without edges produces segmentation results containing some amount of brain matter removed from the result. This is mainly due to the sensitivity of the active contour to intensity values in the sulci present in the brain magnetic resonance image.

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Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images

Cited by 62 publications

References 23 publications

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

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

A Comparative Study on Classification of Image Segmentation Methods With a Focus on Graph Based Techniques

Accuracy based Comparison of Three Brain Extraction Algorithms

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