The cerebellar peduncles are white matter tracts that play an important role in the communication of the cerebellum with other regions of the brain. They can be grouped into three fiber bundles: inferior cerebellar peduncle middle cerebellar peduncle, and superior cerebellar peduncle. Their automatic segmentation on diffusion tensor images would enable a better understanding of the cerebellum and would be less time-consuming and more reproducible than manual delineation. This paper presents a method that automatically labels the three fiber bundles based on the segmentatin results from the diffusion oriented tract segmentation (DOTS) algorithm, which achieves volume segmentation of white matter tracts using a Markov random field (MRF) framework. We use the DOTS labeling result as a guide to determine the classification of fibers produced by wild bootstrap probabilistic tractography. Mean distances from each fiber to each DOTS volume label are defined and then used as features that contribute to classification. A supervised Gaussian classifier is employed to label the fibers. Manually delineated cerebellar peduncles serve as training data to determine the parameters of class probabilities for each label. Fibers are labeled ad the class that has the highest posterior probability. An outlier detection ste[ re,pves fober tracts that belong to noise of that are not modeled by DOTS. Experiments show a successful classification of the cerebellar peduncles. We have also compared results between successive scans to demonstrate the reproducibility of the proposed method.
It has been recently shown that thalamic nuclei can be automatically segmented using diffusion tensor images (DTI)
under the assumption that principal fiber orientation is similar within a given nucleus and distinct between adjacent
nuclei. Validation of these methods, however, is challenging because manual delineation is hard to carry out due to the
lack of images showing contrast between the nuclei. In this paper, we present a novel gray-scale contrast for DTI
visualization that accentuates voxels in which the orientations of the principal eigenvectors are changing, thus providing
an edge map for the delineation of some thalamic nuclei. The method uses the principal fiber orientation computed from
the diffusion tensors computed at each voxel. The three-dimensional orientations of the principal eigenvectors are
represented as five dimensional vectors and the spatial gradient (matrix) of these vectors provide information about
spatial changes in tensor orientation. In particular, an edge map is created by computing the Frobenius norm of this
gradient matrix. We show that this process reveals distinct edges between large nuclei in the thalamus, thereby making
manual delineation of the thalamic nuclei possible. We briefly describe a protocol for the manual delineation of thalamic
nuclei based on this edge map used in conjunction with a registered T1-weighted MR image, and present a preliminary
multi-rater evaluation of the volumes of thalamic nuclei in several subjects.
This paper presents a patch-based non-parametric approach to the correction of intensity inhomogeneity from
magnetic resonance (MR) images of the human brain. During image acquisition, the inhomogeneity present in
the radio-frequency coil, is usually manifested on the reconstructed MR image as a smooth shading effect. This
artifact can significantly deteriorate the performance of any kind of image processing algorithm that uses intensities
as a feature. Most of the current inhomogeneity correction techniques use explicit smoothness assumptions
on the inhomogeneity field, which sometimes limit their performance if the actual inhomogeneity is not smooth, a
problem that becomes prevalent in high fields. The proposed patch-based inhomogeneity correction method does
not assume any parametric smoothness model, instead, it uses patches from an atlas of an inhomogeneity-free
image to do the correction. Preliminary results show that the proposed method is comparable to N3, a current
state of the art method, when the inhomogeneity is smooth, and outperforms N3 when the inhomogeneity
contains non-smooth elements.
KEYWORDS: Image segmentation, Image registration, Transform theory, Magnetic resonance imaging, Brain, Neuroimaging, Medical imaging, Image restoration, Detection and tracking algorithms, Image processing algorithms and systems
Deformable registration techniques play vital roles in a variety of medical imaging tasks such as image fusion,
segmentation, and post-operative surgery assessment. In recent years, mutual information has become one of
the most widely used similarity metrics for medical image registration algorithms. Unfortunately, as a matching
criteria, mutual information loses much of its effectiveness when there is poor statistical consistency and a lack of
structure. This is especially true in areas of images where the intensity is homogeneous and information is sparse.
Here we present a method designed to address this problem by integrating distance transforms of anatomical
segmentations as part of a multi-channel mutual information framework within the registration algorithm. Our
method was tested by registering real MR brain data and comparing the segmentation of the results against that
of the target. Our analysis showed that by integrating distance transforms of the the white matter segmentation
into the registration, the overall segmentation of the registration result was closer to the target than when the
distance transform was not used.
KEYWORDS: Magnetorheological finishing, Monte Carlo methods, Image segmentation, Computer simulations, Brain, Received signal strength, Data modeling, Error analysis, Algorithm development, Matrices
Studies of the size and morphology of anatomical structures rely on accurate and reproducible delineation of the structures,
obtained either by human raters or automatic segmentation algorithms. Measures of reproducibility and variability are
vital aspects of such studies and are usually estimated using repeated scans or repeated delineations (in the case of human
raters). Methods exist for simultaneously estimating the true structure and rater performance parameters from multiple
segmentations and have been demonstrated on volumetric images. In this work, we extend the applicability of previous
methods onto two-dimensional surfaces parameterized as triangle meshes. Label homogeneity is enforced using a Markov
random field formulated with an energy that addresses the challenges introduced by the surface parameterization. The
method was tested using both simulated raters and cortical gyral labels. Simulated raters are computed using a global
error model as well as a novel and more realistic boundary error model. We study the impact of raters and their accuracy
based on both models, and show how effectively this method estimates the true segmentation on simulated surfaces. The
Markov random field formulation was shown to effectively enforce homogeneity for raters suffering from label noise. We
demonstrated that our method provides substantial improvements in accuracy over single-atlas methods for all experimental
conditions.
KEYWORDS: Signal to noise ratio, Diffusion tensor imaging, Diffusion, Error analysis, Monte Carlo methods, Data modeling, Compressed sensing, Tissues, In vivo imaging, Brain
Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture and brain connectivity. Yet DTI
suffers serious limitations in regions of crossing fibers because traditional tensor techniques cannot represent multiple,
independent intra-voxel orientations. Compressed sensing has been proposed to resolve crossing fibers using a tensor
mixture model (e.g., Crossing Fiber Angular Resolution of Intra-voxel structure, CFARI). Although similar in spirit to
deconvolution approaches, CFARI uses sparsity to stabilize estimation with limited data rather than spatial consistency
or limited model order. Here, we extend the CFARI approach to resolve crossing fibers through a strictly positive,
parsimonious mixture model. Together with an optimized preconditioned conjugate gradient solver, estimation error and
computational burden are greatly reduced over the initial presentation. Reliable estimates of intra-voxel orientations are
demonstrated in simulation and in vivo using data representative of typical, low b-value (30 directions, 700 s/mm2)
clinical DTI protocols. These sequences are achievable in 5 minutes at 3 T, and the whole brain CFARI analysis is
tractable for routine analysis. With these improvements, CFARI provides a robust framework for identifying intra-voxel
structure with conventional DTI protocols and shows great promise in helping to resolve the crossing fiber problem in
current clinical imaging studies.
We describe BrainIACS, a web-based medical image processing system that permits and facilitates algorithm developers
to quickly create extensible user interfaces for their algorithms. Designed to address the challenges faced by algorithm
developers in providing user-friendly graphical interfaces, BrainIACS is completely implemented using freely available,
open-source software. The system, which is based on a client-server architecture, utilizes an AJAX front-end written
using the Google Web Toolkit (GWT) and Java Servlets running on Apache Tomcat as its back-end. To enable
developers to quickly and simply create user interfaces for configuring their algorithms, the interfaces are described
using XML and are parsed by our system to create the corresponding user interface elements. Most of the commonly
found elements such as check boxes, drop down lists, input boxes, radio buttons, tab panels and group boxes are
supported. Some elements such as the input box support input validation. Changes to the user interface such as addition
and deletion of elements are performed by editing the XML file or by using the system's user interface creator. In
addition to user interface generation, the system also provides its own interfaces for data transfer, previewing of input
and output files, and algorithm queuing. As the system is programmed using Java (and finally Java-script after
compilation of the front-end code), it is platform independent with the only requirements being that a Servlet
implementation be available and that the processing algorithms can execute on the server platform.
We describe new and freely available software tools for measuring
volumes in subregions of the brain. The method is fast, flexible, and
employs well-studied techniques based on the Talairach-Tournoux
atlas. The software tools are released as plug-ins for MIPAV, a freely available and user-friendly image analysis software package developed by the National Institutes of Health. Our software tools include a digital Talairach atlas that consists of labels for 148 different substructures of the brain at various scales.
KEYWORDS: 3D modeling, Chemical elements, Computer aided design, 3D image reconstruction, Solid modeling, 3D image processing, Reconstruction algorithms, CAD systems, Systems modeling, Logic
Straight lines, rectangles and other simple geometric features are common in man-made environments. Moreover, these geometric features often share particular relationships, for instance parallelism or orthogonality. Such a scene is very constrained, and its 3D description in terms of points is over-determined if the relations are taken into account. Sometimes a constraint solver can maintain the relations, but when estimated positions of the features are unavailable a priori, the knowledge from geometric relations is left unexploited. A better approach would consist in finding a parametric representation that directly merges the relations within a reduced set of parameters, which enforces the relational constraints once and for all. A problem with this idea is that both features and relationships are heterogeneous, so general methods are difficult to design. We propose here a method based on geometric reduction rules for automatically remodeling a scene into such a representation. The method is general for points, linear and planar elements together and can handle at the same time parallelism, orthogonality, colinearity and coplanarity. The number of reduced parameters is equal to the number of degrees of freedom of the system. The approach has been tested with segments, rectangles and points in various scenes, to evaluate the generality and performance of the method.
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