Accurate and consistent skull stripping of serial brain MR images is of great importance in longitudinal studies that aim
to detect subtle brain morphological changes. To avoid inconsistency and the potential bias introduced by independently
performing skull-stripping for each time-point image, we propose an effective method that is capable of skull-stripping
serial brain MR images simultaneously. Specifically, all serial images of the same subject are first affine aligned in a
groupwise manner to a common space to avoid any potential bias introduced by asymmetric transforms. A brain
probability map, which encapsulates prior information gathered from a population of real brain MR images, is then
warped to the aligned serial images for guiding skull-stripping via a deformable surface method. In particular, the same
initial surface meshes representing the initial brain surfaces are first placed on all aligned serial images, and then all
these surface meshes are simultaneously evolved to the respective target brain boundaries, driven by the intensity-based
force, the force from the probability map, as well as the force from the spatial and temporal smoothness. Especially,
imposing the temporal smoothness helps achieve longitudinally consistent results. Evaluations on 20 subjects, each with
4 time points, from the ADNI database indicate that our method gives more accurate and consistent result compared with
3D skull-stripping method. To better show the advantages of our 4D brain extraction method over the 3D method, we
compute the Dice ratio in a ring area (±5mm) surrounding the ground-truth brain boundary, and our 4D method achieves
around 3% improvement over the 3D method. In addition, our 4D method also gives smaller mean and maximal surface-to-
surface distance measurements, with reduced variances.
Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders,
substance use disorders, and normal brain. Using appropriate image processing and statistical tools to analyze
the imaging, behavioral, and clinical data is critical for optimally exploring and interpreting the findings from
those imaging studies. However, the existing imaging processing and statistical methods for analyzing imaging
longitudinal measures are primarily developed for cross-sectional neuroimaging studies. The simple use of these
cross-sectional tools to longitudinal imaging studies will significantly decrease the statistical power of longitudinal
studies in detecting subtle changes of imaging measures and the causal role of time-dependent covariate in disease
process.
The main objective of this paper is to develop longitudinal statistics toolbox, called LSTGEE, for the analysis
of neuroimaging data from longitudinal studies. We develop generalized estimating equations for jointly modeling
imaging measures with behavioral and clinical variables from longitudinal studies. We develop a test procedure
based on a score test statistic and a resampling method to test linear hypotheses of unknown parameters,
such as associations between brain structure and function and covariates of interest, such as IQ, age, gene,
diagnostic groups, and severity of disease. We demonstrate the application of our statistical methods to the
detection of the changes of the fractional anisotropy across time in a longitudinal neonate study. Particularly,
our results demonstrate that the use of longitudinal statistics can dramatically increase the statistical power in
detecting the changes of neuroimaging measures. The proposed approach can be applied to longitudinal data
with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number
of measurements.
This paper proposes a brain image registration algorithm, called RABBIT, which achieves fast and accurate image
registration by using an intermediate template generated by a statistical shape deformation model during the image
registration procedure. The statistical brain shape deformation information is learned by means of principal component
analysis (PCA) from a set of training brain deformations, each of them linking a selected template to an individual brain
sample. Using the statistical deformation information, the template image can be registered to a new individual image by
optimizing a statistical deformation model with a small number of parameters, thus generating an intermediate template
very close to the individual brain image. The remaining shape difference between the intermediate template and the
individual brain is then minimized by a general registration algorithm, such as HAMMER. With the help of the
intermediate template, the registration between the template and individual brain images can be achieved fast and with
similar registration accuracy as HAMMER. The effectiveness of the RABBIT has been evaluated by using both
simulated atrophy data and real brain images. The experimental results show that RABBIT can achieve over five times
speedup, compared to HAMMER, without losing any registration accuracy or statistical power in detecting brain
atrophy.
Traditional fuzzy clustering algorithms have been successfully applied in MR image segmentation for quantitative morphological analysis. However, the clustering results might be biased due to the variability of tissue intensities and anatomical structures. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue probability map constrained clustering algorithm
and apply it to serialMR brain image segmentation for longitudinal study of human brains. The tissue probability maps consist of segmentation priors obtained from a population and reflect the probability of different tissue types. More accurate image segmentation can be achieved by using these segmentation priors in the clustering algorithm. Experimental results of both simulated longitudinal MR brain data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data using the new serial image segmentation algorithm in the framework of CLASSIC show more accurate and robust longitudinal measures.
Brain tissue segmentation of neonate MR images is a challenging task in study of early brain development, due to low
signal contrast among brain tissues and high intensity variability especially in white matter. Among various brain tissue
segmentation algorithms, the atlas-based segmentation techniques can potentially produce reasonable segmentation
results on neonatal brain images. However, their performance on the population-based atlas is still limited due to the high
variability of brain structures across different individuals. Moreover, it may be impossible to generate a reasonable
probabilistic atlas for neonates without tissue segmentation samples. To overcome these limitations, we present a
neonatal brain tissue segmentation method by taking advantage of the longitudinal data available in our study to establish
a subject-specific probabilistic atlas. In particular, tissue segmentation of the neonatal brain is formulated as two iterative
steps of bias correction and probabilistic atlas based tissue segmentation, along with the guidance of brain tissue
segmentation resulted from the later time images of the same subject which serve as a subject-specific probabilistic atlas.
The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with
manual delineation results. Experimental results show that the utilization of a subject-specific probabilistic atlas can
substantially improve tissue segmentation of neonatal brain images.
Group-wise registration has been proposed recently for consistent registration of all images in the same dataset. Since all
images need to be registered simultaneously with lots of deformation parameters to be optimized, the number of images
that the current group-wise registration methods can handle is limited due to the capability of CPU and physical memory
in a general computer. To overcome this limitation, we present a hierarchical group-wise registration method for feasible
registration of large image dataset. Our basic idea is to decompose the large-scale group-wise registration problem into a
series of small-scale registration problems, each of which can be easily solved. In particular, we use a novel affinity
propagation method to hierarchically cluster a group of images into a pyramid of classes. Then, images in the same class
are group-wisely registered to their own center image. The center images of different classes are further group-wisely
registered from the lower level to the upper level of the pyramid. A final atlas for the whole image dataset is thus
synthesized when the registration process reaches the top of the pyramid. By applying this hierarchical image clustering
and atlas synthesis strategy, we can efficiently and effectively perform group-wise registration to a large image dataset
and map each image into the atlas space. More importantly, experimental results on both real and simulated data also
confirm that the proposed method can achieve more robust and accurate registration than the conventional group-wise
registration algorithms.
Diffusion tensor image (DTI) is a powerful tool for quantitatively assessing the integrity of anatomical connectivity
in white matter in clinical populations. The prevalent methods for group-level analysis of DTI are statistical
analyses of invariant measures (e.g., fractional anisotropy) and principal directions across groups. The invariant
measures and principal directions, however, do not capture all information in full diffusion tensor, which can
decrease the statistical power of DTI in detecting subtle changes of white matters. Thus, it is very desirable to
develop new statistical methods for analyzing full diffusion tensors.
In this paper, we develop a set of toolbox, called RADTI, for the analysis of the full diffusion tensors as
responses and establish their association with a set of covariates. The key idea is to use the recent development
of log-Euclidean metric and then transform diffusion tensors in a nonlinear space into their matrix logarithms
in a Euclidean space. Our regression model is a semiparametric model, which avoids any specific parametric
assumptions. We develop an estimation procedure and a test procedure based on score statistics and a resampling
method to simultaneously assess the statistical significance of linear hypotheses across a large region of interest.
Monte Carlo simulations are used to examine the finite sample performance of the test procedure for controlling
the family-wise error rate. We apply our methods to the detection of statistical significance of diagnostic and
age effects on the integrity of white matter in a diffusion tensor study of human immunodeficiency virus.
Feature extraction and selection are of great importance in neuroimage classification for identifying informative features
and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an
integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature
extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction
focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of
brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM
classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also
overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust
performance and optimal selection of parameters involved in feature extraction, selection, and classification, a
bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization,
according to the classification performance measured by the area under the ROC (receiver operating characteristic)
curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's
disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed
algorithm can improve performance of the traditional subspace learning based classification.
A novel hierarchical unbiased group-wise registration is developed to robustly transform each individual image towards
a common space for atlas based analysis. This hierarchical group-wise registration approach consists of two main
components, (1) data clustering to group similar images together and (2) unbiased group-wise registration to generate a
mean image for each cluster. The mean images generated in the lower hierarchical level are regarded as the input
images for the higher hierarchy. In the higher hierarchical level, these input images will be further clustered and then
registered by using the same two components mentioned. This hierarchical bottom-up clustering and within-cluster
group-wise registration is repeated until a final mean image for the whole population is formed. This final mean image
represents the common space for all the subjects to be warped to in order for the atlas based analysis. Each individual
image at the bottom of the constructed hierarchy is transformed towards the root node through concatenating all the
intermediate displacement fields. In order to evaluate the performance of the proposed hierarchical registration in atlas
based statistical analysis, comparisons were made with the conventional group-wise registration in detecting simulated
brain atrophy as well as fractional anisotropy differences between neonates and 1-year-olds. In both cases, the proposed
approach demonstrated improved sensitivity (higher t-scores) than the conventional unbiased registration approach.
KEYWORDS: Biopsy, Prostate, Cancer, Image segmentation, 3D image processing, Ultrasonography, Image registration, 3D modeling, 3D acquisition, Prostate cancer
Prostate cancer is a multifocal disease and lesions are not distributed uniformly within the gland. Several biopsy
protocols concerning spatially specific targeting have been reported urology literature. Recently a statistical
cancer atlas of the prostate was constructed providing voxelwise probabilities of cancers in the prostate. Additionally
an optimized set of biopsy sites was computed with 94 - 96% detection accuracy was reported using only 6-7 needles. Here we discuss the warping of this atlas to prostate segmented side-fire ultrasound images of the patient. A shape model was used to speed up registration. The model was trained from over 38 expert segmented subjects off-line. This training yielded as few as 15-20 degrees of freedom that were optimized to warp the atlas surface to the patient's ultrasound image followed by elastic interpolation of the 3-D atlas. As a result the atlas is completely mapped to the patient's prostate anatomy along with optimal predetermined needle locations for biopsy. These do not preclude the use of additional biopsies if desired. A color overlay of the atlas is also displayed on the ultrasound image showing high cancer zones within the prostate. Finally current biopsy locations are saved in the atlas space and may be used to update the atlas based on the pathology report. In addition to the optimal atlas plan, previous biopsy locations and alternate plans can also be stored in the atlas space and warped to the patient with no additional time overhead.
Prostate cancer is the most commonly diagnosed cancer in males in the United States and the second leading
cause of cancer death. While the exact cause is still under investigation, researchers agree on certain risk factors
like age, family history, dietary habits, lifestyle and race. It is also widely accepted that cancer distribution
within the prostate is inhomogeneous, i.e. certain regions have a higher likelihood of developing cancer. In
this regard extensive work has been done to study the distribution of cancer in order to perform biopsy more
effectively. Recently a statistical cancer atlas of the prostate was demonstrated along with an optimal biopsy
scheme achieving a high detection rate.
In this paper we discuss the complete construction and application of such an atlas that can be used in a
clinical setting to effectively target high cancer zones during biopsy. The method consists of integrating intensity
statistics in the form of cancer probabilities at every voxel in the image with shape statistics of the prostate in
order to quickly warp the atlas onto a subject ultrasound image. While the atlas surface can be registered to a
pre-segmented subject prostate surface or instead used to perform segmentation of the capsule via optimization
of shape parameters to segment the subject image, the strength of our approach lies in the fast mapping of cancer
statistics onto the subject using shape statistics. The shape model was trained from over 38 expert segmented
prostate surfaces and the atlas registration accuracy was found to be high suggesting the use of this method to
perform biopsy in near real time situations with some optimization.
A comprehensive framework is proposed for the spatial normalization of diffusion tensor (DT) brain images using tensor-derived tissue attributes. In this framework, the brain tissues are first classified into three categories: the white matter (WM), the gray matter (GM), and the cerebral-spinal fluid (CSF) using the anisotropy and diffusivity information derived from the full tensor. The tissue attributes obtained from this anisotropic segmentation are then incorporated into a very-high-dimensional elastic registration method to produce a spatial deformation field. Finally, the rotational
component in the deformation field, together with the estimated underlying fiber direction, is used to determine an appropriate tensor reorientation. This framework has been assessed quantitatively and qualitatively based on a sequence of experiments. A simulated experiment has been performed to evaluate the accuracy of the spatial warping by examining the variation between deformation fields. To verify the tensor reorientation, especially, in the anisotropic microstructures of WM fiber tissues, an experiment has been designed to compare the fiber tracts generated from the DT template and the normalized DT subjects in some regions of interest (ROIs). Finally, this method has been applied to spatially normalize 31 subjects to a common space, the case in which there exist large deformations between subjects and the existing approaches are normally difficult to achieve satisfactory results. The average across the individual normalized DT images shows a significant improvement in signal-to-noise ratio (SNR).
The motivation of this work is to register MR brain tumor images with a brain atlas. Such a registration method can
make possible the pooling of data from different brain tumor patients into a common stereotaxic space, thereby enabling
the construction of statistical brain tumor atlases. Moreover, it allows the mapping of neuroanatomical brain atlases into
the patient's space, for segmenting brains and thus facilitating surgical or radiotherapy treatment planning. However, the
methods developed for registration of normal brain images are not directly applicable to the registration of a normal atlas
with a tumor-bearing image, due to substantial dissimilarity and lack of equivalent image content between the two
images, as well as severe deformation or shift of anatomical structures around the tumor. Accordingly, a model that can
simulate brain tissue death and deformation induced by the tumor is considered to facilitate the registration. Such tumor
growth simulation models are usually initialized by placing a small seed in the normal atlas. The shape, size and location
of the initial seed are critical for achieving topological equivalence between the atlas and patient's images. In this study,
we focus on the automatic estimation of these parameters, pertaining to tumor simulation. In particular, we propose an
objective function reflecting feature-based similarity and elastic stretching energy and optimize it with APPSPACK
(Asynchronous Parallel Pattern Search), for achieving significant reduction of the computational cost. The results
indicate that the registration accuracy is high in areas around the tumor, as well as in the healthy portion of the brain.
DSA images suffer from challenges like system X-ray noise and artifacts due to patient movement. In this paper, we present a two-step strategy to improve DSA image quality. First, a hierarchical deformable registration algorithm is used to register the mask frame and the bolus frame before subtraction. Second, the resulted DSA image is further enhanced by background diffusion and nonlinear normalization for better visualization. Two major changes are made in the hierarchical deformable registration algorithm for DSA images: 1) B-Spline is used to represent the deformation field in order to produce the smooth deformation field; 2) two features are defined as the attribute vector for each point in the image, i.e., original image intensity and gradient. Also, for speeding up the 2D
image registration, the hierarchical motion compensation algorithm is implemented by a multi-resolution framework. The proposed method has been evaluated on a database of 73 subjects by quantitatively measuring signal-to-noise (SNR) ratio. DSA embedded with proposed strategies demonstrates an improvement of 74.1% over conventional DSA in terms of SNR. Our system runs on Eigen's DSA workstation using C++ in Windows environment.
Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.
Parcellation of the cortex has received a great deal of attention in magnetic resonance (MR) image analysis, but its usefulness has been limited by time-consuming algorithms that require manual labeling. An automatic labeling scheme is necessary to accurately and consistently parcellate a large number of brains. The large variation of cortical folding patterns makes automatic labeling a challenging problem, which cannot be solved by deformable atlas registration alone. In this work, an automated classification scheme that consists of a mix of both atlas driven and data driven methods is proposed to label the sulcal regions, which are defined as the gray matter regions of the cortical surface surrounding each sulcus. The premise for this algorithm is that sulcal regions can be classified according to the pattern of anatomical features (e.g. supramarginal gyrus, cuneus, etc.) associated with each region. Using a nearest-neighbor approach, a sulcal region is classified as being in the same class as the sulcus from a set of training data which has the nearest pattern of anatomical features. Using just one subject as training data, the algorithm correctly labeled 83% of the regions that make up the main sulci of the cortex.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.