Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET

Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.     

A robust method for extraction and automatic segmentation of brain images

A new protocol is introduced for brain extraction and automatic tissue segmentation of MR images. For the brain extraction algorithm, proton density and T2-weighted images are used to generate a brain mask encompassing the full intracranial cavity. Segmentation of brain tissues into gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) is accomplished on a T1-weighted image after applying the brain mask. The fully automatic segmentation algorithm is histogram-based and uses the Expectation Maximization algorithm to model a four-Gaussian mixture for both global and local histograms. The means of the local Gaussians for GM, WM, and CSF are used to set local thresholds for tissue classification. Reproducibility of the extraction procedure was excellent, with average variation in intracranial capacity (TIC) of 0.13 and 0.66% TIC in 12 healthy normal and 33 Alzheimer brains, respectively. Repeatability of the segmentation algorithm, tested on healthy normal images, indicated scan-rescan differences in global tissue volumes of less than 0.30% TIC. Reproducibility at the regional level was established by comparing segmentation results within the 12 major Talairach subdivisions. Accuracy of the algorithm was tested on a digital brain phantom, and errors were less than 1% of the phantom volume. Maximal Type I and Type II classification errors were low, ranging between 2.2 and 4.3% of phantom volume. The algorithm was also insensitive to variation in parameter initialization values. The protocol is robust, fast, and its success in segmenting normal as well as diseased brains makes it an attractive clinical application.     

Diffusion tensor imaging-based tissue segmentation: validation and application to the developing child and adolescent brain

We present and validate a novel diffusion tensor imaging (DTI) approach for segmenting the human whole-brain into partitions representing grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The approach utilizes the contrast among tissue types in the DTI anisotropy vs. diffusivity rotational invariant space. The DTI-based whole-brain GM and WM fractions (GMf and WMf) are contrasted with the fractions obtained from conventional magnetic resonance imaging (cMRI) tissue segmentation (or clustering) methods that utilized dual echo (proton density-weighted (PDw)), and spin-spin relaxation-weighted (T2w) contrast, in addition to spin-lattice relaxation weighted (T1w) contrasts acquired in the same imaging session and covering the same volume. In addition to good correspondence with cMRI estimates of brain volume, the DTI-based segmentation approach accurately depicts expected age vs. WM and GM volume-to-total intracranial brain volume percentage trends on the rapidly developing brains of a cohort of 29 children (6-18 years). This approach promises to extend DTI utility to both micro and macrostructural aspects of tissue organization.     

Evidence for cervical cord tissue disorganisation with aging by diffusion tensor MRI

This study investigated the influence of normal aging on cervical cord volumetry and diffusivity changes and assessed whether magnetic resonance imaging (MRI) abnormalities of the aging cervical cord and brain are associated. Conventional and diffusion tensor (DT) MRI of the brain and cervical cord were acquired from 96 healthy subjects (age range=13-70 years). Cross-sectional area, mean diffusivity (MD) and fractional anisotropy (FA) of the cervical cord were measured. Volumetry and diffusivity metrics were also obtained for the brain white matter (WM) and grey matter (GM) (overall and cortical). No cervical cord lesions were seen on conventional MR images from all subjects. Degenerative vertebral column changes (not associated to cord compression) were found in 41 subjects (43%). Average FA of the cervical cord, but not average MD and cross-sectional area, was correlated with age (r=-0.70, p<0.001). Additionally, T2 brain lesion volume, normalised brain volume (NBV), normalised global and cortical brain GM volumes and average MD of the brain GM and WM also correlated with age (r values ranging from -0.83 to 0.62). Only brain WM average FA was weakly correlated with cervical cord average FA (r=0.25, p=0.02). The final multivariate model retained cord average FA (r=-0.37, p<0.001), normalised cortical GM volume (r=-0.56, p<0.001) and NBV (r=-0.22, p=0.04) as independent correlates of age (r2=0.76). Cervical cord is vulnerable to aging. The decrease of FA, in the absence of atrophy and MD changes, suggests gliosis as the most likely pathological feature of the aging cord.     

Relating neocortical pathology to disability progression in multiple sclerosis using MRI

Cortical grey matter (cGM) develops a substantial burden of pathology in multiple sclerosis (MS). Previous cross-sectional studies have suggested a relationship between measures of cortical atrophy and disability. Our objective was to develop a method for automatically measuring the apparent cGM thickness as well as the integrity of the interface between cGM and subcortical white matter (GM/WM) both globally and regionally on T(1)-weighted MRI, and use this method in a longitudinal investigation of how these measures differed between patients with stable MS and patients with progressing disability. Measurements were made over the whole brain and for anatomically specified cortical regions, both cross-sectionally at baseline and longitudinally on two MRI scans performed on average 1 year apart. We found a higher average rate of apparent loss of cGM thickness across the whole brain in the group that progressed over the interscan interval compared to the group that remained stable (progressing = -3.13 +/- 2.88%/year, stable = 0.06 +/- 2.31%/year, P = 0.002). This difference was detected with regional measures in parietal and precentral cortex. In contrast, change in the GM/WM interface integrity did not show detectable regional differences, although the group of MS patients whose disability progressed showed a significant decrease in GM/WM interface integrity compared to the stable group (P = 0.003). Regional measures of apparent loss of cGM thickness enhance sensitivity to cortical pathological changes. A measure of integrity offers a new index of disease-associated cortical changes at the GM/WM interface. The results suggest that progression of disability in MS is associated with the progression of MRI-detectable cortical pathology.     

Age- and sex-related effects on the neuroanatomy of healthy elderly

Effects of age and sex, and their interaction on the structural brain anatomy of healthy elderly were assessed thanks to a cross-sectional study of a cohort of 662 subjects aged from 63 to 75 years. T1- and T2-weighted MRI scans were acquired in each subject and further processed using a voxel-based approach that was optimized for the identification of the cerebrospinal fluid (CSF) compartment. Analysis of covariance revealed a classical neuroanatomy sexual dimorphism, men exhibiting larger gray matter (GM), white matter (WM), and CSF compartment volumes, together with larger WM and CSF fractions, whereas women showed larger GM fraction. GM and WM were found to significantly decrease with age, while CSF volume significantly increased. Tissue probability map analysis showed that the highest rates of GM atrophy in this age range were localized in primary cortices, the angular and superior parietal gyri, the orbital part of the prefrontal cortex, and in the hippocampal region. There was no significant interaction between "Sex" and "Age" for any of the tissue volumes, as well as for any of the tissue probability maps. These findings indicate that brain atrophy during the seventh and eighth decades of life is ubiquitous and proceeds at a rate that is not modulated by "Sex".     

Global average gray and white matter N-acetylaspartate concentration in the human brain

Since the amino acid derivative N-acetylaspartate (NAA) is almost exclusive to neuronal cells in the adult mammalian brain and its concentration has shown local (or global) abnormalities in most focal (or diffuse) neurological diseases, it is considered a specific neuronal marker. Yet despite its biological and clinical prominence, the relative NAA concentration in the gray and white matter (GM, WM) remains controversial, with each reported to be higher than, equal to, or less than the other. To help resolve the controversy and importantly, access the NAA in both compartments in their entirety, we introduce a new approach to distinguish and quantify the whole-brain average GM and WM NAA concentration by integrating MR-image segmentation, localized and non-localized quantitative (1)H-MRS. We demonstrate and validate the method in ten healthy volunteers (5 women) 27+/-6 years old (mean+/-standard-deviation) at 1.5T. The results show that the healthy adult human brain comprises significantly less WM, 39+/-3%, than GM 60+/-4% by volume (p<0.01). Furthermore, the average NAA concentration in the WM, 9.5+/-1.0 mM, is significantly lower than in GM, 14.3+/-1.1 mM (p<0.01).     

Validating cortical surface analysis of medial prefrontal cortex.

This paper describes cortical analysis of 19 high resolution MRI subvolumes of medial prefrontal cortex (MPFC), a region that has been implicated in major depressive disorder. An automated Bayesian segmentation is used to delineate the MRI subvolumes into cerebrospinal fluid (CSF), gray matter (GM), white matter (WM), and partial volumes of either CSF/GM or GM/WM. The intensity value at which there is equal probability of GM and GM/WM partial volume is used to reconstruct MPFC cortical surfaces based on a 3-D isocontouring algorithm. The segmented data and the generated surfaces are validated by comparison with hand segmented data and semiautomated contours, respectively. The L(1) distances between Bayesian and hand segmented data are 0.05-0.10 (n = 5). Fifty percent of the voxels of the reconstructed surface lie within 0.12-0.28 mm (n = 14) from the semiautomated contours. Cortical thickness metrics are generated in the form of frequency of occurrence histograms for GM and WM labelled voxels as a function of their position from the cortical surface. An algorithm to compute the surface area of the GM/WM interface of the MPFC subvolume is described. These methods represent a novel approach to morphometric chacterization of regional cortex features which may be important in the study of psychiatric disorders such as major depression.     

Semi-automatic brain region extraction (SABRE) reveals superior cortical and deep gray matter atrophy in MS

In multiple sclerosis (MS), atrophy occurs in various cortical and subcortical regions. However, it is unclear whether this is mostly due to gray (GM) or white matter (WM) loss. Recently, a new semi-automatic brain region extraction (SABRE) technique was developed to quantify parenchyma volume in 13 hemispheric regions. This study utilized SABRE and tissue segmentation to examine whether regional brain atrophy in MS is mostly due to GM or WM loss, correlated with disease duration, and moderated by disease course. We studied 68 MS patients and 39 normal controls with 1.5 T brain MRI. As expected, MS diagnosis was associated with significantly lower (P < 0.001) regional brain parenchymal fractions (RBPFs). While significant findings emerged in 11 GM comparisons, only four WM comparisons were significant. The largest mean RBPF percent differences between groups (MS < NC) were in the posterior basal ganglia/thalamus region (-19.3%), superior frontal (-15.7%), and superior parietal (-14.3%) regions. Logistic regression analyses showed GM regions were more predictive of MS diagnosis than WM regions. Eight GM RBPFs were significantly correlated (P < 0.001) with disease duration compared to only one WM region. Significant trends emerged for differences in GM, but not WM between secondary progressive (SP) and relapsing-remitting MS patients. Percent differences in GM between the two groups were largest in superior frontal (-9.9%), medial superior frontal (-6.5%), and superior parietal (-6.1%) regions, with SP patients having lower volumes. Overall, atrophy in MS is diffuse and mostly related to GM loss particularly in deep GM and superior frontal-parietal regions.     

White matter lesion extension to automatic brain tissue segmentation on MRI

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.     

Brain tissue segmentation based on DTI data

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided.     

Voxel-based analysis derived from fractional anisotropy images of white matter volume changes with aging

Although age-related effects on brain volume have been extensively investigated post mortem and in vivo using magnetic resonance imaging (MRI), regional and temporal patterns of white matter (WM) volume changes with aging are not defined yet. The aim of this study was to assess the topographical distribution of age-related WM volume changes using a recently developed voxel-based method to obtain estimates of WM fiber bundle volumes using diffusion tensor (DT) MRI. Brain conventional and DT MRI were obtained from 84 healthy subjects (mean age=44 years, range=13-70). Linear and non-linear relationships between age and WM fiber bundle volume changes were tested. A negative linear correlation was found between age and WM volume decline in the corona radiata, anterior cingulum, body and crus of the fornix and left superior cerebellar peduncle. A positive linear correlation was found between age and volume increase of the right deep temporal association fibers. The non-linear regression analysis also showed age-related changes of the genu of the corpus callosum and fitted better the volume changes of the right deep temporal association fibers. WM volume decline with age is unevenly distributed across brain regions. Our approach holds promise to gain additional information on the pathological changes associated to neurological disorders of the elderly.     

Infant brain probability templates for MRI segmentation and normalization

Spatial normalization and segmentation of infant brain MRI data based on adult or pediatric reference data may not be appropriate due to the developmental differences between the infant input data and the reference data. In this study we have constructed infant templates and a priori brain tissue probability maps based on the MR brain image data from 76 infants ranging in age from 9 to 15 months. We employed two processing strategies to construct the infant template and a priori data: one processed with and one without using a priori data in the segmentation step. Using the templates we constructed, comparisons between the adult templates and the new infant templates are presented. Tissue distribution differences are apparent between the infant and adult template, particularly in the gray matter (GM) maps. The infant a priori information classifies brain tissue as GM with higher probability than adult data, at the cost of white matter (WM), which presents with lower probability when compared to adult data. The differences are more pronounced in the frontal regions and in the cingulate gyrus. Similar differences are also observed when the infant data is compared to a pediatric (age 5 to 18) template. The two-pass segmentation approach taken here for infant T1W brain images has provided high quality tissue probability maps for GM, WM, and CSF, in infant brain images. These templates may be used as prior probability distributions for segmentation and normalization; a key to improving the accuracy of these procedures in special populations.     

Grey matter volume alterations in CADASIL: a voxel-based morphometry study

CADASIL is a hereditary disease characterized by cerebral subcortical microangiopathy leading to early onset cerebral strokes and progressive severe cognitive impairment. Until now, only few studies have investigated the extent and localization of grey matter (GM) involvement. The purpose of our study was to evaluate GM volume alterations in CADASIL patients compared to healthy subjects. We also looked for correlations between global and regional white matter (WM) lesion load and GM volume alterations. 14 genetically proved CADASIL patients and 12 healthy subjects were enrolled in our study. Brain MRI (1.5 T) was acquired in all subjects. Optimized-voxel based morphometry method was applied for the comparison of brain volumes between CADASIL patients and controls. Global and lobar WM lesion loads were calculated for each patient and used as covariate-of-interest for regression analyses with SPM-8. Compared to controls, patients showed GM volume reductions in bilateral temporal lobes (p < 0.05; FDR-corrected). Regression analysis in the patient group revealed a correlation between total WM lesion load and temporal GM atrophy (p < 0.05; uncorrected), not between temporal lesion load and GM atrophy. Temporal GM volume reduction was demonstrated in CADASIL patients compared to controls; it was related to WM lesion load involving the whole brain but not to lobar and, specifically, temporal WM lesion load. Complex interactions between sub-cortical and cortical damage should be hypothesized.     

Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification

Accurate reconstruction of the inner and outer cortical surfaces of the human cerebrum is a critical objective for a wide variety of neuroimaging analysis purposes, including visualization, morphometry, and brain mapping. The Anatomic Segmentation using Proximity (ASP) algorithm, previously developed by our group, provides a topology-preserving cortical surface deformation method that has been extensively used for the aforementioned purposes. However, constraints in the algorithm to ensure topology preservation occasionally produce incorrect thickness measurements due to a restriction in the range of allowable distances between the gray and white matter surfaces. This problem is particularly prominent in pediatric brain images with tightly folded gyri. This paper presents a novel method for improving the conventional ASP algorithm by making use of partial volume information through probabilistic classification in order to allow for topology preservation across a less restricted range of cortical thickness values. The new algorithm also corrects the classification of the insular cortex by masking out subcortical tissues. For 70 pediatric brains, validation experiments for the modified algorithm, Constrained Laplacian ASP (CLASP), were performed by three methods: (i) volume matching between surface-masked gray matter (GM) and conventional tissue-classified GM, (ii) surface matching between simulated and CLASP-extracted surfaces, and (iii) repeatability of the surface reconstruction among 16 MRI scans of the same subject. In the volume-based evaluation, the volume enclosed by the CLASP WM and GM surfaces matched the classified GM volume 13% more accurately than using conventional ASP. In the surface-based evaluation, using synthesized thick cortex, the average difference between simulated and extracted surfaces was 4.6 +/- 1.4 mm for conventional ASP and 0.5 +/- 0.4 mm for CLASP. In a repeatability study, CLASP produced a 30% lower RMS error for the GM surface and a 8% lower RMS error for the WM surface compared with ASP.     

Linking white matter tracts to associated cortical grey matter: a tract extension methodology

Quantitative diffusion analysis of white matter (WM) tracts has been utilised in many diseases for determining damage to, and changes in, WM tracts throughout the brain. However, there are limited studies investigating associations between quantitative measures in WM tracts and anatomically linked grey matter (GM), due to the difficulty in determining GM regions connected with a given WM tract.This work describes a straightforward method for extending a WM tract through GM based on geometry. The tract is extended by following a straight line from each point on the tract boundary to the outer boundary of the cortex. A comparison between a multiplanar 2D approach and a 3D method was made. This study also tested an analysis pipeline from tracking WM tracts to quantifying magnetisation transfer ratios (MTR) in the associated cortical GM, and assessed the applicability of the method to healthy control subjects. Tract and associated cortical volumes and MTR values for the cortico-spinal tracts, genu and body of the corpus callosum were extracted; the between-subjects standard deviation was calculated.It was found that a multiplanar 2D approach produced a more anatomically plausible volume of GM than a 3D approach, at the expense of possible overestimation of the GM volume. The between-subjects standard deviation of the tract specific quantitative measurements (from both the WM and GM masks) ranged between 1.2 and 7.3% for the MTR measures, and between 10 and 45% for the absolute volume measures.The results show that the method can be used to produce anatomically plausible extensions of the WM tracts through the GM, and regions defined in this way yield reliable estimates of the MTR from the regions.     

Automatic segmentation and reconstruction of the cortex from neonatal MRI

Segmentation and reconstruction of cortical surfaces from magnetic resonance (MR) images are more challenging for developing neonates than adults. This is mainly due to the dynamic changes in the contrast between gray matter (GM) and white matter (WM) in both T1- and T2-weighted images (T1w and T2w) during brain maturation. In particular in neonatal T2w images WM typically has higher signal intensity than GM. This causes mislabeled voxels during cortical segmentation, especially in the cortical regions of the brain and in particular at the interface between GM and cerebrospinal fluid (CSF). We propose an automatic segmentation algorithm detecting these mislabeled voxels and correcting errors caused by partial volume effects. Our results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic expectation maximization (EM) scheme. Quantitative validation against manual segmentation demonstrates good performance (the mean Dice value: 0.758+/-0.037 for GM and 0.794+/-0.078 for WM). The inner, central and outer cortical surfaces are then reconstructed using implicit surface evolution. A landmark study is performed to verify the accuracy of the reconstructed cortex (the mean surface reconstruction error: 0.73 mm for inner surface and 0.63 mm for the outer). Both segmentation and reconstruction have been tested on 25 neonates with the gestational ages ranging from approximately 27 to 45 weeks. This preliminary analysis confirms previous findings that cortical surface area and curvature increase with age, and that surface area scales to cerebral volume according to a power law, while cortical thickness is not related to age or brain growth.     

Informed constrained spherical deconvolution (iCSD)

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels.In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data.Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.     

Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images

Current semiautomated magnetic resonance (MR)-based brain segmentation and volume measurement methods are complex and not sufficiently accurate for certain applications. We have developed a simpler, more accurate automated algorithm for whole-brain segmentation and volume measurement in T(1)-weighted, three-dimensional MR images. This histogram-based brain segmentation (HBRS) algorithm is based on histograms and simple morphological operations. The algorithm's three steps are foreground/background thresholding, disconnection of brain from skull, and removal of residue fragments (sinus, cerebrospinal fluid, dura, and marrow). Brain volume was measured by counting the number of brain voxels. Accuracy was determined by applying HBRS to both simulated and real MR data. Comparing the brain volume rendered by HBRS with the volume on which the simulation is based, the average error was 1.38%. By applying HBRS to 20 normal MR data sets downloaded from the Internet Brain Segmentation Repository and comparing them with expert segmented data, the average Jaccard similarity was 0.963 and the kappa index was 0.981. The reproducibility of brain volume measurements was assessed by comparing data from two sessions (four total data sets) with human volunteers. Intrasession variability of brain volumes for sessions 1 and 2 was 0.55 +/- 0.56 and 0.74 +/- 0.56%, respectively; the mean difference between the two sessions was 0.60 +/- 0.46%. These results show that the HBRS algorithm is a simple, fast, and accurate method to determine brain volume with high reproducibility. This algorithm may be applied to various research and clinical investigations in which brain segmentation and volume measurement involving MRI data are needed.