The world’s aging population has given rise to an increasing awareness towards neurodegenerative disorders, including Alzheimers Disease (AD). Treatment options for AD are currently limited, but it is believed that future success depends on our ability to detect the onset of the disease in its early stages. The most frequently used tools for this include neuropsychological assessments, along with genetic, proteomic, and image-based diagnosis. Recently, the applicability of Diffusion Magnetic Resonance Imaging (dMRI) analysis for early diagnosis of AD has also been reported. The sensitivity of dMRI to the microstructural organization of cerebral tissue makes it particularly well-suited to detecting changes which are known to occur in the early stages of AD. Existing dMRI approaches can be divided into two broad categories: region-based and tract-based. In this work, we propose a new approach, which extends region-based approaches to the simultaneous characterization of multiple brain regions. Given a predefined set of features derived from dMRI data, we compute the probabilistic distances between different brain regions and treat the resulting connectivity pattern as an undirected, fully-connected graph. The characteristics of this graph are then used as markers to discriminate between AD subjects and normal controls (NC). Although in this preliminary work we omit subjects in the prodromal stage of AD, mild cognitive impairment (MCI), our method demonstrates perfect separability between AD and NC subject groups with substantial margin, and thus holds promise for fine-grained stratification of NC, MCI and AD populations.
Multi-shell diffusion imaging (MSDI) allows to characterize the subtle tissue properties of neurons along with providing valuable information about the ensemble average diffusion propagator. Several methods, both para- metric and non-parametric, have been proposed to analyze MSDI data. In this work, we propose a hybrid model, which is non-parametric in the angular domain but parametric in the radial domain. This has the advantage of allowing arbitrary number of fiber orientations in the angular domain, yet requiring as little as two b-value shells in the radial (q-space) domain. Thus, an extensive sampling of the q-space is not required to compute the diffusion propagator. This model, which we term as the dual-spherical" model, requires estimation of two functions on the sphere to completely (and continuously) model the entire q-space diffusion signal. Specifically, we formulate the cost function so that the diffusion signal is guaranteed to monotonically decrease with b-value for user-defined range of b-values. This is in contrast to other methods, which do not enforce such a constraint, resulting in in-accurate modeling of the diffusion signal (where the signal values could potentially increase with b-value). We also show the relation of our proposed method with that of diffusional kurtosis imaging and how our model extends the kurtosis model. We use the standard spherical harmonics to estimate these functions on the sphere and show its efficacy using synthetic and in-vivo experiments. In particular, on synthetic data, we computed the normalized mean squared error and the average angular error in the estimated orientation distribution function (ODF) and show that the proposed technique works better than the existing work which only uses a parametric model for estimating the radial decay of the diffusion signal with b-value.
High angular resolution diffusion imaging (HARDI) improves upon more traditional diffusion tensor imaging (DTI) in its ability to resolve the orientations of crossing and branching neural fibre tracts. The HARDI signals are measured over a spherical shell in q-space, and are usually used as an input to q-ball imaging (QBI) which allows estimation of the diffusion orientation distribution functions (ODFs) associated with a given region-of interest. Unfortunately, the partial nature of single-shell sampling imposes limits on the estimation accuracy. As a result, the recovered ODFs may not possess sufficient resolution to reveal the orientations of fibre tracts which cross each other at acute angles. A possible solution to the problem of limited resolution of QBI is provided by means of spherical deconvolution, a particular instance of which is sparse deconvolution. However, while capable of yielding high-resolution reconstructions over spacial locations corresponding to white matter, such methods tend to become unstable when applied to anatomical regions with a substantial content of isotropic diffusion. To resolve this problem, a new deconvolution approach is proposed in this paper. Apart from being uniformly stable across the whole brain, the proposed method allows one to quantify the isotropic component of cerebral diffusion, which is known to be a useful diagnostic measure by itself.
This paper presents a novel pipeline for the registration of diffusion tensor images (DTI) with large pathological
variations to normal controls based on the use of a novel feature map derived from white matter (WM) fiber
tracts. The research presented aims towards an atlas based DTI analysis of subjects with considerable brain
pathologies such as tumors or hydrocephalus. In this paper, we propose a novel feature map that is robust against
variations in WM fiber tract integrity and use these feature maps to determine a landmark correspondence using
a 3D point correspondence algorithm. This correspondence drives a deformation field computed using Gaussian
radial basis functions(RBF). This field is employed as an initialization to a standard deformable registration
method like demons. We present early preliminary results on the registration of a normal control dataset to a
dataset with abnormally enlarged lateral ventricles affected by fatal demyelinating Krabbe disease. The results
are analyzed based on a regional tensor matching criterion and a visual assessment of overlap of major WM fiber
tracts. While further evaluation and improvements are necessary, the results presented in this paper highlight
the potential of our method in handling registration of subjects with severe WM pathology.
The level set method is a popular technique used in medical image segmentation; however, the numerics involved
make its use cumbersome. This paper proposes an approximate level set scheme that removes much of the
computational burden while maintaining accuracy.
Abandoning a floating point representation for the signed distance function, we use integral values to represent
the signed distance function. For the cases of 2D and 3D, we detail rules governing the evolution and maintenance
of these three regions. Arbitrary energies can be implemented in the framework.
This scheme has several desirable properties: computations are only performed along the zero level set;
the approximate distance function requires only a few simple integer comparisons for maintenance; smoothness
regularization involves only a few integer calculations and may be handled apart from the energy itself; the zero
level set is represented exactly removing the need for interpolation off the interface; and evolutions proceed on
the order of milliseconds per iteration on conventional uniprocessor workstations.
To highlight its accuracy, flexibility and speed, we demonstrate the technique on intensity-based segmentations
under various statistical metrics. Results for 3D imagery show the technique is fast even for image volumes.
This paper presents a novel analytic technique to perform shape-driven segmentation. In our approach, shapes
are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity
based probability distributions are then employed to convert a given test volume into a binary map representation,
and a novel energy functional is proposed whose minimum can be analytically computed to obtain the desired
segmentation in the shape space. We compare the proposed method with the log-likelihood based energy to
elucidate some key differences. Our algorithm is applied to the segmentation of brain caudate nucleus and
hippocampus from MRI data, which is of interest in the study of schizophrenia and Alzheimer's disease. Our
validation (we compute the Hausdorff distance and the DICE coefficient between the automatic segmentation
and ground-truth) shows that the proposed algorithm is very fast, requires no initialization and outperforms the
log-likelihood based energy.
The level set method for curve evolution is a popular technique used in image processing applications. However,
the numerics involved make its use in high performance systems computationally prohibitive. This paper proposes
an approximate level set scheme that removes much of the computational burden while maintaining accuracy.
Abandoning a floating point representation for the signed distance function, we use the integral values to
represent the interior, zero level set, and exterior. We detail rules governing the evolution and maintenance of
these three regions. Arbitrary energies can be implemented with the definition of three operations: initialize
iteration, move points in, move points out.
This scheme has several nice properties. First, computations are only performed along the zero level set.
Second, this approximate distance function representation requires only a few simple integer comparisons for
maintenance. Third, smoothness regularization involves only a few integer calculations and may be handled apart
from the energy itself. Fourth, the zero level set is represented exactly removing the need for interpolation off the
interface. Lastly, evolution proceeds on the order of milliseconds per iteration using conventional uniprocessor
To highlight its accuracy, flexibility and speed, we demonstrate the technique on standard intensity tracking
and stand alone segmentation.
The Geometric Active Contour (GAC) framework, which utilizes image information, has proven to be quite valuable for performing segmentation. However, the use of image information alone often leads to poor segmentation results in the presence of noise, clutter or occlusion. The introduction of shapes priors in the contour evolution proved to be an effective way to circumvent this issue. Recently, an algorithm was proposed, in which linear PCA (principal component analysis) was performed on training sets of data and the shape statistics thus obtained were used in the segmentation process. This approach was shown to convincingly capture small variations in the shape of an object. However, linear PCA assumes that the distribution underlying the variation in shapes is Gaussian. This assumption can be over-simplifying when shapes undergo complex variations. In the present work, we derive the steps for using Kernel PCA to in the GAC framework to introduce prior shape knowledge. Several experiments were performed using different training-sets of shapes. Starting with any initial contour, we show that the contour evolves to adopt a shape that is faithful to the elements of the training set. The proposed shape prior method leads to better performances than the one involving linear PCA.
Mercer kernels are used for a wide range of image and signal processing tasks like de-noising, clustering, discriminant
analysis etc. These algorithms construct their solutions in terms of the expansions in a high-dimensional
feature space F. However, many applications like kernel PCA (principal component analysis) can be used more
effectively if a pre-image of the projection in the feature space is available. In this paper, we propose a novel
method to reconstruct a unique approximate pre-image of a feature vector and apply it for statistical shape
analysis. We provide some experimental results to demonstrate the advantages of kernel PCA over linear PCA
for shape learning, which include, but are not limited to, ability to learn and distinguish multiple geometries of
shapes and robustness to occlusions.
Proc. SPIE. 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
KEYWORDS: Image processing algorithms and systems, Detection and tracking algorithms, Sensors, Magnetic resonance imaging, Image segmentation, 3D modeling, Medical imaging, Computer engineering, 3D image processing, Algorithms
In this paper we present an algorithm for 3D medical image segmentation based on an affine invariant flow. The algorithm is simple to implement and semi-automatic. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The surface flow is obtained by minimizing a global energy with respect to an affine invariant metric. Affine invariant edge detectors for 3-dimensional objects are also computed which have the same qualitative behavior as the Euclidean edge detectors. Results on artificial and real MRI images show that the algorithm performs well, both in terms of accuracy and robustness to noise.