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The use of a single labeled volume (“atlas”) is limited in registration-based segmentation because it is hard for one atlas to represent the whole data population, especially if input images observe large variation. Moreover, the choice of volume to label biases the algorithm. Multi-atlas segmentation has emerged as an alternative but it has a similar drawback due to combinatory combinations of different...
Several methods have been proposed over the years for segmentation of vessels, many of them based on scale-space. However, none of the existing methods for blood vessel segmentation is appropriate for extension to bifurcation detection. Other existing bifurcation detection algorithms use an inherently serial “track and detect” approach, which also requires a seed point. We present for the first time...
The uncertainty in registration of medical images may contain important information in cases where clinical decisions are based on registered data. Posing the registration problem in a Bayesian framework allows characterization of the posterior distribution of the deformation parameters which represents the uncertainty of the registration. Uncertainty estimation approaches are complicated by the need...
High angular resolution diffusion imaging (HARDI) is known to excel in delineating multiple diffusion flows through a given location within the white matter of the brain. Unfortunately, many current methods of implementation of HARDI require collecting a relatively large number of diffusion-encoded images, which is in turn translated in prohibitively long acquisition times. As a possible solution...
A method is presented which fits a surrogate-driven motion model to a standard clinically-acquired cone-beam computed tomography scan, taken prior to a fraction of stereotactic body radiotherapy treatment. The motion model can account for motion hysteresis, and is intended to drive gated or tracked radiotherapy treatments. The method can be applied in cases with or without implanted markers. Results...
We propose a novel motion correction approach for dynamic emission tomography images that takes advantage of the underlying compartmental models of tracer kinetics. Our algorithm uses a simultaneous segmentation registration paradigm. The key idea of our approach is that, unlike the standard frame-by-frame (FbF) based registration methods, we avoid choosing a reference frame and essentially create...
In this paper we propose a novel segmentation method that integrates prior shape knowledge obtained from a 3D statistical model into the Markov Random Field (MRF) segmentation framework to deal with severe artifacts, noise and shape deformations. The statistical model is learned using a Probabilistic Principal Component Analysis (PPCA), which allows us to reconstruct the optimal shape and to compute...
MRI tractography is the only method that noninvasively maps neural connections in the brain. Interest in its use for diagnosis and treatment of neurological disease is growing rapidly. Probabilistic tractography provides quantitative measures that can be interpreted as the strength or reliability of connections, but Monte Carlo implementations can require impractical computation times and have difficulty...
In segmentation of magnetic resonance brain images, it is important to maintain topology of the segmented structures. In this work, we present a framework to segment multiple objects in a brain image while preserving the topology of each object as given in an initial topological template. The framework combines the advantages of digital topology and several existing techniques in graph cuts segmentation...
We design and evaluate a scalar measure for HARDI data called Geodesic Concentration (GC) that characterizes the integrity of white matter (WM), by quantifying the anisotropic concentration of water diffusion. Mathematically, GC is defined as the concentration relative to the peak, identified in the HARDI model fitted to the data. As GC reflects the degree of directionally coherent diffusion, it is...
Malignant melanoma (MM) is one of the most frequent types of cancers among the world's white population. Dermoscopy is a noninvasive method for early recognition of MM by which physicians assess the skin lesion according to the skin subsurface features. The presence or absence of “streaks” is one of the most important dermoscopic criteria for the diagnosis of MM. We develop a machine-learning approach...
We introduce a framework for analyzing symmetry of 3D anatomical structures using elastic deformations of their boundaries (surfaces). The basic idea is to define a space of parameterized surfaces and to compute geodesic paths between the objects and their arbitrary reflections using a Riemannian structure. Elastic matching, based on optimal (non-linear) re-parameterizations (grid deformations) of...
We present a new sparse shape modeling framework on the Laplace-Beltrami (LB) eigenfunctions. Traditionally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes by forming a Fourier series expansion. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency...
General linear modeling (GLM) is one of the most commonly used approaches to perform voxel based analyses (VBA) for hypotheses testing in neuroimaging. In this paper we tie support vector machine based regression (SVR) and classical significance testing to provide the benefits of max margin estimation in the GLM setting. Using Welch-Satterthwaite approximations, we compute degrees of freedom (df)...
In this paper we propose a method to register a pair of images unseen to the original dataset based on a generative manifold model. The basic premise of this approach is to design an image distance metric using a weighted sum of similarity and smoothness terms derived from a diffeomorphic registration of pairwise images. A refined image distance matrix based on this metric can be adopted as an input...
Sparse representation has proven to be a powerful mathematical framework for studying high-dimensional data and uncovering its structures. Some recent research has shown its promise in discriminating image patterns. This paper presents an approach employing sparse appearance representation for segmenting left ventricular endocardial and epicardial boundaries from 2D echocardiographic sequences. It...
Multi-fiber models have been introduced as an efficient and interpretable way of representing the diffusion signal in areas with crossing fibers. However, no metric has been provided to use multi-fiber features in registration. The normalized correlation coefficient is commonly used in registration of scalar images due to its invariance under linear transformations of the intensities. In this paper,...
Many previous studies in multiple sclerosis (MS) have focused on the relationship between white matter lesion volume and clinical parameters, but few have investigated the independent contribution of the spatial dispersion of lesions to patient disability. In this study, we examine the ability of four different measures of lesion dispersion including one connectedness-based measure (compactness),...
This paper presents a fiber tracking method that solves fiber crossing and avoids anatomically improbable fiber tracts using clinical-quality diffusion tensor images. The method tracks fiber pathways under the guidance of global information from volumetric tract segmentation obtained from the algorithm of diffusion oriented tract segmentation (DOTS). New primary diffusion directions (PDD) are calculated...
We propose a Riemannian framework for analyzing orientation distribution functions (ODFs), or corresponding probability density functions (PDFs), in HARDI for use in comparing, interpolating, averaging, and denoising. Recent approaches based on the Fisher-Rao Riemannian metric result in geodesic paths that have limited biological interpretations. As an alternative, we develop a framework where we...
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