251 research outputs found
Ensemble average propagator estimation of axon diameter in diffusion MRI: implications and limitations
No full textDiffusion Magnetic Resonance Imaging (dMRI) has beenused to infer the axon diameter of the tissues using biophysicalmodels of diffusion. In recent years, a new methodwas proposed for estimating axon diameter directly fromthe ensemble average propagator (EAP), and in particularfrom one of its derived index, the return to the axis probability(RTAP). In this work we study the effect that differentacquisition parameters have on the estimation of the axondiameter with three different EAP models: diffusion tensorimaging (DTI), 3D-SHORE, and MAPMRI. We quantify theeffect that moving from the long diffusion time conditionhas on the diameter estimation by simulating the diffusionsignal inside impermeable cylinders. Results on in-vivo datashows that EAP models estimation of axon diameter is heavilyinfluenced by extra-axonal water and incoherence of fiberorientation, which hides the intra-axonal signal
Using 3D-SHORE and MAP-MRI to obtain both tractography and microstructural contrasts from clinical DMRI acquisitions
No full textDiffusion MRI (dMRI) is used to characterize the directionalityand microstructural properties of brain white matter (WM)by measuring the diffusivity of water molecules. In clinicalpractice the number of dMRI samples that can be obtained islimited, and one often uses short scanning protocols that acquirejust 32 to 64 different gradient directions using a singlegradient strength (b-value). Such 'single shell' scanning protocolsrestrict one to use methods that have assumptions onthe radial decay of the dMRI signal over different b-values,which introduces estimation biases. In this work we show,that by simply spreading the same number of samples overmultiple b-values (i.e. multi-shell) we can accurately estimateboth the WM directionality using 3D-SHORE and characterizethe radially dependent diffusion microstructure measuresusing MAP-MRI. We validate our approach by undersamplingboth noisy synthetic and human brain data of the HumanConnectome Project, proving this approach is well-suited forclinical applications
Recursivity and PDE's in image processing
No full textRecursive filtering structures reduce drastically the computational effort required for different tasks in image processing. These operations are done with a fixed number of operations per output point independently of the size of the neighbourhood considered. In this paper we show that implicit numerical implementations of some partial differential equations (PDEs) provide algorithms that can be interpreted in terms of recursive filters. We show, in particular, that the classical second order recursive filter introduced by Deriche (1987, 1990) is in fact a particular implementation of the heat equation. Using the well-known Neumann boundary condition for the heat equation, we propose some new implementation of the filter. We extend this linear filter to a nonlinear recursive smoothing filter, following the general idea of anisotropic diffusion. We present some comparison results with the classical Perona-Malik model.24824
A decision support system for site selection of large-scale infrastructure facilities using natural language
No full textSpatial problems are often characterised by incomplete information, multiple conflicting evaluation criteria and a heterogeneous group of decision-makers. Implementation of existing analytical decision–making methods in a Spatial Decision Support System has been characterised by difficulties when dealing with uncertainty, criteria standardization, and group decision-making where there is no consensus. Another problem is the real or perceived difficulty of using such systems, leading to poor uptake by decision-makers. This paper discusses the development of a spatial decision support system (SDSS) using a new natural language approach to mitigate the abovementioned difficulties. The system is designed to aid site selection for large-scale infrastructure facilities at a strategic level, using a fuzzy multicriteria, multi-decision-maker framework and linguistic methods. We describe the theoretical basis of our approach, and its practical implementation in a GIS-based System. A real world site selection problem involving the location of a new industrial facility at Brisbane Airport (Australia) is also worked through
Analysis of surface extraction methods based on gradient operators for computed tomography in metrology applications
No full textAmong the multiple factors influencing the accuracy of Computed Tomography measurements, the surface extraction process is a significant contributor. The location of the surface for metrological applications is generally based on the definition of a gray value as a characteristic of similarity to define the regions of interest. A different approach is to perform the detection or location of the surface based on the discontinuity or gradient. In this paper, an adapted 3D Deriche algorithm based on gradient information is presented and compared with a previously developed adapted Canny algorithm for different surface types. Both algorithms have been applied to nine calibrated workpieces with different geometries and materials. Both the systematic error and measurement uncertainty have been determined. The results show a significant reduction of the deviations obtained with the Deriche-based algorithm in the dimensions defined by flat surfaces
Dense disparity map estimation respecting image discontinuities: a PDE and scale-space based approach
No full textWe present an energy based approach to estimate a dense disparity map from a set of two weakly calibrated stereoscopic images while preserving its discontinuities resulting from image boundaries. We first derive a simplified expression for the disparity that allows us to estimate it from a stereo pair of images using an energy minimization approach. We assume that the epipolar geometry is known, and we include this information in the energy model. Discontinuities are preserved by means of a regularization term based on the Nagel-Enkelmann operator, We investigate the associated Euter-Lagrange equation of the energy functional, and we approach the solution of the underlying partial differential equation (PDE) using a gradient descent method. The resulting parabolic problem has a unique solution. In order to reduce the risk, to be trapped within some irrelevant local minima during the iterations, we use a focusing strategy based on a linear scale-space. Experimental results on both synthetic and real images are presented to illustrate the capabilities of this PDE and scale-space based method. (C) 2002 Elsevier Science (USA).213SCI
SIFER: Scale-Invariant Feature detector with Error Resilience
No full textsponsorship: The authors would like to thank Rachid Deriche from INRIA, Prof. Lucas J. Van Vliet and Prof. Ian T. Young from TU/Delft for discussions and answering our emails regarding the approximation design methods for the filters. Author Bert Geelen was supported by IWT SBO-project 100021 "CHAMELEON". (IWT SBO-project|100021)status: Publishe
Actes du deuxième colloque africain sur la recherche en informatique = Proceedings of the second African Conference on research in computer science
No full textThe cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems
No full textMotivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co
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