Automatic detection of brain contours in MRI data sets

Mersereau, R.M.; Eisner, Robert L.; Lewine, Richard

doi:10.1007/bfb0033753

Cited by 85 publications

(110 citation statements)

References 7 publications

Supporting

Mentioning

107

Contrasting

Unclassified

Order By: Relevance

“…A number of techniques have been proposed, manual or semi-automated methods are labor-intensive, operator-dependent, time Brummer et al [30] Histogram-based thresholding and morphological operations.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

2015

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The method, automatic detection of brain contours in MRI datasets developed by Brummer et al [30] is one of the first commonly used methods for skull stripping. It consists of histogram-based thresholding and morphological operations.…”

Section: Morphology-based Methodsmentioning

confidence: 99%

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

2015

View full text Add to dashboard Cite

show abstract

“…In order to describe the modifications, a synopsis of the method 13 along with the modifications is provided. The steps involved were: manually defining the breast volume used for determining the breast skin thickness; 17,19 segmenting the breast volume from the background (air), for which we used an algorithm 20,21 different from the prior study; 13 transforming each coronal slice to polar coordinates, for which we used 1 • angular steps and applied and a 3 × 3 median filter prior to polar transform; followed by applying a one-dimensional (1D) derivative filter to obtain the gradient image. For segmenting the skin layer from the gradient image, we used a search range of up to 5.5-6.8 mm (20-25 voxels of 0.273 mm size) from the outer skin layer (air-skin boundary) to determine the voxel with the minimum (negative) gradient representative of the inner skin layer (skin-fat boundary).…”

Section: Methodsmentioning

confidence: 99%

Technical Note: Skin thickness measurements using high‐resolution flat‐panel cone‐beam dedicated breast CTa)

Shi¹,

Vedantham²,

Karellas³

et al. 2013

Medical Physics

View full text Add to dashboard Cite

Purpose: To determine the mean and range of location-averaged breast skin thickness using highresolution dedicated breast CT for use in Monte Carlo-based estimation of normalized glandular dose coefficients. Methods: This study retrospectively analyzed image data from a clinical study investigating dedicated breast CT. An algorithm similar to that described by Huang et al. ["The effect of skin thickness determined using breast CT on mammographic dosimetry," Med. Phys. 35(4), 1199-1206 (2008)] was used to determine the skin thickness in 137 dedicated breast CT volumes from 136 women. The location-averaged mean breast skin thickness for each breast was estimated and the study population mean and range were determined. Pathology results were available for 132 women, and were used to investigate if the distribution of location-averaged mean breast skin thickness varied with pathology. The effect of surface fitting to account for breast curvature was also studied. Results: The study mean (± interbreast SD) for breast skin thickness was 1.44 ± 0.25 mm (range: 0.87-2.34 mm), which was in excellent agreement with Huang et al. Based on pathology, pair-wise statistical analysis (Mann-Whitney test) indicated that at the 0.05 significance level, there were no significant difference in the location-averaged mean breast skin thickness distributions between the groups: benign vs malignant (p = 0.223), benign vs hyperplasia (p = 0.651), hyperplasia vs malignant (p = 0.229), and malignant vs nonmalignant (p = 0.172). Conclusions: Considering this study used a different clinical prototype system, and the study participants were from a different geographical location, the observed agreement between the two studies suggests that the choice of 1.45 mm thick skin layer comprising the epidermis and the dermis for breast dosimetry is appropriate. While some benign and malignant conditions could cause skin thickening, in this study cohort the location-averaged mean breast skin thickness distributions did not differ significantly with pathology. The study also underscored the importance of considering breast curvature in estimating breast skin thickness.

show abstract

“…Fig. 3(b) shows the skull boundary detected by automatic global thresholding [22] and subsequent post-processing [1]. Then the skull boundary is approximated by an ellipse.…”

Section: Proof: One Can Writementioning

confidence: 99%