Decomposition of higher-order homogeneous tensors and applications to HARDI

High Angular Resolution Diffusion Imaging (HARDI) holds the promise to provide insight in connectivity of the human brain in vivo. Based on this technique a number of different approaches has been proposed to estimate the ¿ber orientation distribution, which is crucial for ¿ber tracking. A spherical harmonic representation is convenient for regularization and the construction of orientation distribution functions (ODFs), whereas maxima detection and ¿ber tracking techniques are most naturally formulated using a tensor representation. We give an analytical formulation to bridge the gap between the two representations, which admits regularization and ODF construction directly in the tensor basis

On the Riemannian rationale for diffusion tensor imaging

One of the approaches in the analysis of brain diffusion MRI data is to consider white matter as a Riemannian manifold, with a metric given by the inverse of the diffusion tensor. Such a metric is used for white matter tractography and connectivity analysis. Although this choice of metric is heuristically justified it has not been derived from first principles. We propose a modification of the metric tensor motivated by the underlying mathematics of diffusion

Riemann-Finsler geometry and its applications to diffusion magnetic resonance imaging

Riemannian geometry has become a popular mathematical framework for the analysis of diffusion tensor images (DTI) in diffusion weighted magnetic resonance imaging (DWMRI). If one declines from the a priori constraint to model local anisotropic diffusion in terms of a 6-degrees-of-freedom rank-2 DTI tensor, then Riemann-Finsler geometry appears to be the natural extension. As such it provides an interesting alternative to the Riemannian rationale in the context of the various high angular resolution diffusion imaging (HARDI) schemes proposed in the literature. The main advantages of the proposed Riemann-Finsler paradigm are its manifest incorporation of the DTI model as a limiting case via a "correspondence principle" (operationalized in terms of a vanishing Cartan tensor), and its direct connection to the physics of DWMRI expressed by the (appropriately generalized) Stejskal-Tanner equation and Bloch-Torrey equations furnished with a diffusion term

Inferring Mental Representational Structure of the Self in Time, Space, and Social Domains via a Modified Redundancy Gain Paradigm

The ability to project oneself into an alternative situation is an essential human capacity. While research showing that such abilities base human decision making is abundant, the cognitive organization of the self across social, temporal, and spatial domains constituting the basic materials for self-projection is not clear. The current study introduces a new paradigm to gauge the representational overlaps among social (me myself), temporal (me now) and spatial (me here) selves by utilizing a shape-label matching task in a modified redundancy gain paradigm. Based on the level of redundancy gain effects, we infer a representational overlap among social, temporal, and spatial selves in a systematic way. Our results showed that the spatial self resides at the core of the self-representation which conceptually extends to the temporal and ultimately, to the social self, echoing the human developmental stages of self-representation. This novel finding advances the understanding and theorizing of the self-concept as an orderly structured mental construct

Learning from Live Coding

The pace of engagement with new digital technologies in music education is proving to be slow. A key issue - and a major barrier to entry - is teacher skills and confidence to work with technology creatively. Following a concerted campaign from the technology industry, in spring 2013 the UK government agreed to introduce the subject of computing into the national curriculum for all children at the age of five. This chapter describes, interprets and theorises the digital skills and engagement with live coding performance in music

Cardiac motion estimation using multi-scale feature points

Heart illnesses influence the functioning of the cardiac muscle and are the major causes of death inthe world. Optic flow methods are essential tools to assess and quantify the contraction of the cardiacwalls, but are hampered by the aperture problem. Harmonic phase (HARP) techniques measure thephase in magnetic resonance (MR) tagged images. Due to the regular geometry, patterns generated bya combination of HARPs and sine HARPs represent a suitable framework to extract landmark features.In this paper we introduce a new aperture-problem free method to study the cardiac motion by trackingmulti-scale features such as maxima, minima, saddles and corners, on HARP and sine HARP taggedimages

Математична модель контактного з’єднання метало-пластмасових циліндричних оболонок

We consider alpha scale spaces, a parameterized class (alpha is an element of (0, 1]) of scale space representations beyond the well-established Gaussian scale space, which are generated by the alpha-th power of the minus Laplace operator on a bounded domain using the Neumann boundary condition. The Neumann boundary condition ensures that there is no grey-value flux through the boundary. Thereby no artificial grey-values from outside the image affect the evolution proces, which is the case for the alpha scale spaces on an unbounded domain. Moreover, the connection between the a scale spaces which is not trivial in the unbounded domain case, becomes straightforward: The generator of the Gaussian semigroup extends to a compact, self-adjoint operator on the Hilbert space L-2(Omega) and therefore it has a complete countable set of eigen functions. Taking the alpha-th power of the Gaussian generator simply boils down to taking the alpha-th power of the corresponding eigenvalues. Consequently, all alpha scale spaces have exactly the same eigen-modes and can be implemented simultaneously as scale dependent Fourier series. The only difference between them is the (relative) contribution of each eigen-mode to the evolution proces. By introducing the notion of (non-dimensional) relative scale in each a scale space, we are able to compare the various alpha scale spaces. The case alpha = 0.5, where the generator equals the square root of the minus Laplace operator leads to Poisson scale space, which is at least as interesting as Gaussian scale space and can be extended to a (Clifford) analytic scale space

Cardiac motion estimation using covariant derivatives and Helmholtz decomposition

The investigation and quantification of cardiac movement is important for assessment of cardiac abnormalities and treatment effectiveness. Therefore we consider new aperture problem-free methods to track cardiac motion from 2-dimensional MR tagged images and corresponding sine-phase images. Tracking is achieved by following the movement of scale-space maxima, yielding a sparse set of linear features of the unknown optic flow vector field. Interpolation/reconstruction of the velocity field is then carried out by minimizing an energy functional which is a Sobolev-norm expressed in covariant derivatives (rather than standard derivatives). These covariant derivatives are used to express prior knowledge about the velocity field in the variational framework employed. They are defined on a fiber bundle where sections coincide with vector fields. Furthermore, the optic flow vector field is decomposed in a divergence free and a rotation free part, using our multi-scale Helmholtz decomposition algorithm that combines diffusion and Helmholtz decomposition in a single non-singular analytic kernel operator. Finally, we combine this multi-scale Helmholtz decomposition with vector field reconstruction (based on covariant derivatives) in a single algorithm and present some experiments of cardiac motion estimation. Further experiments on phantom data with ground truth show that both the inclusion of covariant derivatives and the inclusion of the multi-scale Helmholtz decomposition improves the optic flow reconstruction

The Multiscale Morphology Filter: Identifying and Extracting Spatial Patterns in the Galaxy Distribution

We present here a new method, MMF, for automatically segmenting cosmic structure into its basic components: clusters, filaments, and walls. Importantly, the segmentation is scale independent, so all structures are identified without prejudice as to their size or shape. The method is ideally suited for extracting catalogues of clusters, walls, and filaments from samples of galaxies in redshift surveys or from particles in cosmological N-body simulations: it makes no prior assumptions about the scale or shape of the structures.}Comment: Replacement with higher resolution figures. 28 pages, 17 figures. For Full Resolution Version see: http://www.astro.rug.nl/~weygaert/tim1publication/miguelmmf.pd