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Rationale and Objectives: Although segmentation algorithms for cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM) on unenhanced computed tomographic (CT) images exist, there is no complete research in this area. To take into account poor image contrast and intensity variability on CT scans, the aim of this study was to derive and validate a novel, automatic, adaptive, and robust algorithm., Materials and Methods: Unenhanced CT scans of normal subjects from two different centers were used. The algorithm developed uses adaptive thresholding, connectivity, and domain knowledge and is based on heuristics on the shape of CT histogram. The slope of the intensity histogram corresponding to the three-dimensional largest connected region in a variable CSF intensity range is tracked to determine the critical intensity, which serves as an initial classifier of CSF-WM. Thresholds of CSF, WM, and GM are then optimally derived to minimize classification overlap. Multiple, null, and erroneous classifications are resolved by applying domain knowledge., Results: The ground-truth regions with the minimal partial volume effect were used to evaluate segmentation results using the statistical markers. Average sensitivity, Dice index, and specificity, respectively, for the first center were 95.7%, 97.0%, and 98.6% for CSF; 96.1%, 97.3%, and 98.8% for WM; and 95.2%, 94.3%, and 92.8% for GM. The results were consistent for the second data center., Conclusions: The algorithm automatically identifies CSF, WM, and GM on unenhanced CT images with high accuracy, is robust to data from different scanners, does not require any parameter setting, and takes about 5 minutes in MATLAB to process a 512 × 512 × 30 scan. The algorithm has potential use in research and clinical applications., (Copyright © 2010 AUR. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: Accurate segmentation of the brain ventricular system on computed tomographic (CT) imaging is useful in neurodiagnosis and neurosurgery. Manual segmentation is time consuming, usually not reproducible, and subjective. Because of image noise, low contrast between soft tissues, large interslice distance, large shape, and size variations of the ventricular system, no automatic method is presently available. The authors propose a model-guided method for the automated segmentation of the ventricular system., Materials and Methods: Fifty CT scans of patients with strokes at different sites were collected for this study. Given a brain CT image, its ventricular system was segmented in five steps: (1) a predefined volumetric model was registered (or deformed) onto the image; (2) according to the deformed model, eight regions of interest were automatically specified; (3) the intensity threshold of cerebrospinal fluid was calculated in a region of interest and used to segment all regions of cerebrospinal fluid from the entire brain volume; (4) each ventricle was segmented in its specified region of interest; and (5) intraventricular calcification regions were identified to refine the ventricular segmentation., Results: Compared to ground truths provided by experts, the segmentation results of this method achieved an average overlap ratio of 85% for the entire ventricular system. On a desktop personal computer with a dual-core central processing unit running at 2.13 GHz, about 10 seconds were required to analyze each data set., Conclusion: Experiments with clinical CT images showed that the proposed method can generate acceptable results in the presence of image noise, large shape, and size variations of the ventricular system, and therefore it is potentially useful for the quantitative interpretation of CT images in neurodiagnosis and neurosurgery., (Copyright (c) 2010 AUR. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: Accurate, free of observer's bias, and fast identification of acute infarct is critical in visual and automatic processing of stroke images. An automatic and rapid algorithm has been developed to identify the infarct slices and the hemisphere in diffusion-weighted imaging (DWI) scans., Materials and Methods: Thirty-six DWI scans were acquired from five centers with the slice thickness of 4-14 mm. We also derive images from the original scans to assess the accuracy of the algorithm by using a wide range of infarct size and number of artifacts per unit area. Based on the difference in percentile characteristics of intensity normalized (infarct/noninfarct) images, two parameters are defined: R(s) for infarct slice identification and R(h) for infarct hemisphere identification. Using the identified infarct slices the infarct hemisphere is subsequently determined., Results: The average sensitivity and specificity for slice and hemisphere identification were 98.1%, 51.4% and 91.7%, 91.7%, respectively. The processing time is approximately 3-5 seconds on Matlab platform and on VC++ it is predicted approximately 10 milliseconds. Based on simulation study, we can infer that the algorithm produces accurate results in most of the situations although the sensitivity goes down by approximately 15% when the infarct size is small (<2-3% of image area) and the artifacts per unit area are large., Conclusions: The proposed algorithm applied as a preprocessor can be useful to: 1) estimate location (hemisphere) and extent of infarct (number and location of slices), 2) reduce time and labor of infarct volume study, 3) cross-check visual interpretation, 4) form a part of an infarct segmentation module, and 5) improve localization of the midsagittal plane.

Rationale and Objectives: Accurate identification of infarcted regions of the brain is critical in management of stroke patients. An efficient and fast method for identification and segmentation of infarcts in the diffusion-weighted images (DWI) is proposed., Materials and Methods: Thirteen stroke patients were studied. DWI scans were acquired with a slice thickness of 5 mm. We have used a probabilistic neural network for selecting infarct slices and an adaptive (two-level) Gaussian mixture model for segmentation of the infarcts. Statistical analysis, such as identification of distribution, first-order statistics calculation, and receiver operating characteristic curve analysis, was performed., Results: The average dice index is about 0.6, and average sensitivity and specificity are about 81% and 99%, respectively. The value of sensitivity and dice index are influenced by the number of false positives and false negatives. Because artifacts and infarcts have similar imaging characteristics, it is difficult to completely eliminate the artifacts. The accuracy of localization is nearly 100% as there were only two false-positive and three false-negative slices of all 381 slices. The algorithm takes about 1 minute in the Matlab computing environment to process a volume., Conclusion: A method to localize and segment the acute brain infarcts is proposed. The method aids the clinician in reducing the time needed to localize and segment the infarcts. The speed of localization and segmentation can be enhanced further by implementing the algorithm in VC++ and using fast algorithms for selection of Gaussian mixture model parameters.

Rationale and Objectives: A method for atlas-assisted analysis of stroke magnetic resonance images that is a part of a stroke computer-assisted diagnosis system supporting rapid and quantitative checking of thrombolysis conditions is presented., Materials and Methods: Two brain atlases are used for analysis: atlas of anatomy (AA) and atlas of blood supply territories (BSTs). To map these atlases onto scans, two methods are used at present: (1) fast Talairach transformation and (2) midsagittal plane and brain's bounding box matching. After atlas-to-scan mapping, both atlases are superimposed onto the studied images and can be used to get their underlying anatomy and BSTs. To speed up the process of analysis, the system automatically analyzes entire regions occupied by the infarct and penumbra., Results: By using both atlases, the system calculates the following values for each infarct and penumbra region: (1) names of all anatomic structures and BSTs within the region, (2) volumes of occupancy for each structure and territory, and (3) percentages of occupancy for each structure and territory. In addition, the system calculates the infarct-middle cerebral artery (MCA) territory ratio for diffusion-weighted images and the penumbra-MCA territory ratio for perfusion images. Atlas-assisted analysis is fast, and calculations take less than 10 seconds., Conclusion: This method potentially facilitates and speeds up stroke data analysis, as well as supports decision making.

Rationale and Objectives: The human brain demonstrates approximate bilateral symmetry of anatomy, function, neurochemical activity, and electrophysiology. This symmetry reflected in radiological images may be affected by pathology. Hence quantitative analysis of brain symmetry may enable the normal and pathological brain discrimination. We propose a method based on the Jeffreys divergence measure (J-divergence), which attempts to quantify "approximate symmetry" and also aids to classify the brain as bilaterally symmetrical/asymmetrical (normal/abnormal)., Materials and Methods: The dataset included studies of 101 patients (59 without detectable pathologies and 42 with different abnormalities). First, the midsagittal plane is computed for the volume data that divides the head into two hemispheres. The J-divergence is calculated from the density functions of intensities of both the hemispheres. Statistical analysis was conducted to find the best distribution for normal/abnormal datasets., Results: Statistical tests showed that the lognormal distribution best characterizes the values of the J-divergence for both normal and abnormal cases, and the threshold value for the Jeffreys divergence measure to classify the brains with and without detectable pathologies is T = 0.007. The threshold value had a sensitivity of 88.1% and specificity of 90.9%., Conclusion: The proposed method is fast and simple to compute. The high sensitivity and specificity indicate the results are encouraging. This method can be used for the initial analysis of data, detection of pathology, classification of dataset as presumably normal/abnormal, and localization of abnormality.

Rationale and Objectives: A near real-time and fully automatic method for calculation of the midsagittal plane (MSP) for magnetic resonance (MR) diffusion and perfusion images is introduced., Materials and Methods: The method is based on the Kullback-Leibler's (KL) measure quantifying the difference between two intensity distributions. The MSP is a sagittal plane with the highest KL measure. The method was validated quantitatively for 61 diffusion-weighted imaging (DWI), cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), peak height (PKHT), and time to peak (TPP) data sets of 11 stroke patients based on the ground truth provided by two raters., Results: Average angular errors are less than 1 degrees for DWI and less than 2 degrees for CBF and CBV. Average distance errors measured in the worst case (on the brain's bounding box) are less than 2.5 mm for DWI and less than 5 mm for CBF and CBV. This algorithmic accuracy is at the level of interrater variability. Results obtained for the other perfusions maps (MTT, PKHT, TTP) were inferior; therefore, processing of CBF or CBV is preferred for accurate and robust calculation of the MSP from perfusion maps. Calculation of the MSP takes about half a second on a standard computer., Conclusions: The proposed method is near real-time and fully automatic, and neither user interaction nor parameter setting is needed. It does not require preprocessing of data. The method potentially is useful in rapid and automated processing of MR stroke diffusion and perfusion images.

Rationale and Objectives: This study explores morphological relationships and structural variability of the corpus callosum (CC), fornix (Fo), anterior (AC), and posterior commissures (PC)., Materials and Methods: These structures are extracted automatically on the midsagittal plane. The CC and Fo are modeled using best-fit ellipses. The parameters characterizing these structures and relationships among them are points, distances, angles, and eccentricities. The minimum, maximum and mean values, standard deviations, and coefficients of variation for all parameters are calculated for 62 diversified MRI datasets. Subsequently, the regression analysis and parameter distribution study are performed., Results: The parameters have at least 10% variations. The major axis of CC and eccentricities of CC and Fo vary much less than the other parameters The major axis of CC is approximately parallel to the AC-PC line. The mean eccentricity of each of CC and Fo is greater than 0.95. The most significant correlation (P < .05) is observed between various angles and the angle between the major axes of CC and Fo. The correlation is also significant between other angles and distances. The Weibull distribution characterizes the major axis of CC, and distance between the AC and the most superior point of CC. Distribution of angle between the major axes of CC and Fo is log (logistic), and normal for the AC-PC distance., Conclusions: The AC-PC distance, used prevalently for brain normalization, is not correlated with any parameters except with the distance between the AC and the most superior point on the body of the CC with P < .05.

Rationale and Objective: Accurate identification of the anterior commissure (AC) and posterior commissure (PC) is critical in neuroradiology, functional neurosurgery, human brain mapping, and neuroscience research. Moreover, major stereotactic brain atlases are based on the AC and PC. Our goal is to provide an algorithm for a rapid, robust, accurate and automatic identification of AC and PC., Materials and Method: The method exploits anatomical and radiological properties of AC, PC and surrounding structures, including morphological variability. The localization is done in two stages: coarse and fine. The coarse stage locates the AC and PC on the midsagittal plane by analyzing their relationships with the corpus callosum, fornix, and brainstem. The fine stage refines the AC and PC in a well-defined volume of interest, analyzing locations of lateral and third ventricles, interhemispheric fissure, and massa intermedia., Results: The algorithm was developed using simple operations, like histogramming, thresholding, region growing, 1D projections. It was tested on 94 diversified T1W and SPGR datasets. After the fine stage, 71 (76%) volumes had an error between 0-1 mm for the AC and 55 (59%) for the PC. The mean errors were 1.0 mm (AC) and 1.0 mm (PC). The accuracy has improved twice due to fine stage processing. The algorithm took about 1 second for coarse and 4 seconds for fine processing on P4, 2.5 GHz., Conclusion: The use of anatomical and radiological knowledge including variability in algorithm formulation aids in localization of structures more accurately and robustly. This fully automatic algorithm is potentially useful in clinical setting and for research.

Rationale and Objectives: Determination of distorted brain anatomy surrounding a tumor causing a mass effect is much more difficult than interpretation of normal brain scans, particularly because this distortion is not easily predictable a tumor may be located in any place and vary substantially in size, shape, and radiological appearance. The objective of our work is to provide a qualitative means for rapid estimation of brain anatomy distorted by tumor., Materials and Methods: Toward achieving this objective, we use an electronic and deformable brain atlas of gross anatomy along with a fast atlas-to-data warping technique. The deformed atlas determines the distorted anatomy surrounding a tumor and can be used for structure labeling (naming). The warping algorithm uses the Talairach transformation followed by three-dimensional nonlinear tumor deformation based on a geometric assumption that the tumor, delineated on radiological images, compresses its surrounding tissues radially., Results: The approach is implemented and a dedicated application is developed. It processes the data automatically in five steps: (1) load data, (2) set the Talairach landmarks and perform the Talairach transformation, (3) segment the tumor, (4) warp the scan nonlinearly in three dimensions, and (5) explore the scan. The approach is very fast, and a magnetic resonance imaging scan is processed in 10-15 seconds on a standard personal computer. It is fully automatic and gives the user control over the entire process., Conclusion: Despite its limitations in modeling and validation, this practical solution provides a rapid and potentially useful qualitative assessment of anatomy deformed by a mass effect tumor.