Satellite image georegistration from coast-line codification

This paper presents a contour-based approach for automatic image registration in satellite oceanography. Accurate image georegistration is an essential step to increase the eff ectiveness of all the image processing methods that aggregate information from diff erent sources, i.e. applying data fusion techniques. In our approach the images description is based on main contours extracted from coast-line. Each contour is codifi ed by a modifi ed chain-code, and the result is a discrete value sequence. The classical registration techniques were area-based, and the registration was done in a 2D domain (spatial and/or transformed); this approach is feature-based, and the registration is done in a 1D domain (discrete sequences). This new technique improves the registration results. It allows the registration of multimodal images, and the registration when there are occlusions and gaps in the images (i.e. due to clouds), or the registration on images with moderate perspective changes. Finally, it has to be pointed out that the proposed contour-matching technique assumes that a reference image, containing the coastlines of the input image geographical area, is available

Satellite image georegistration from coast-line codification

Martech 2007 International Workshop on Marine Technology, 15-16 november 2007, Vilanova i la Geltrú, Spain.-- 2 pages, 3 figuresThis paper presents a contour-based approach for automatic image registration in satellite oceanography. Accurate image georegistration is an essential step to increase the eff ectiveness of all the image processing methods that aggregate information from diff erent sources, i.e. applying data fusion techniques. In our approach the images description is based on main contours extracted from coast-line. Each contour is codifi ed by a modifi ed chain-code, and the result is a discrete value sequence. The classical registration techniques were area-based, and the registration was done in a 2D domain (spatial and/or transformed); this approach is feature-based, and the registration is done in a 1D domain (discrete sequences). This new technique improves the registration results. It allows the registration of multimodal images, and the registration when there are occlusions and gaps in the images (i.e. due to clouds), or the registration on images with moderate perspective changes. Finally, it has to be pointed out that the proposed contour-matching technique assumes that a reference image, containing the coastlines of the input image geographical area, is availablePeer reviewe

Weighted frames of exponentials and stable recovery of multidimensional functions from nonuniform Fourier samples

In this paper, we consider the problem of recovering a compactly supported multivariate function from a collection of pointwise samples of its Fourier transform taken nonuniformly. We do this by using the concept of weighted Fourier frames. A seminal result of Beurling shows that sample points give rise to a classical Fourier frame provided they are relatively separated and of sufficient density. However, this result does not allow for arbitrary clustering of sample points, as is often the case in practice. Whilst keeping the density condition sharp and dimension independent, our first result removes the separation condition and shows that density alone suffices. However, this result does not lead to estimates for the frame bounds. A known result of Groechenig provides explicit estimates, but only subject to a density condition that deteriorates linearly with dimension. In our second result we improve these bounds by reducing the dimension dependence. In particular, we provide explicit frame bounds which are dimensionless for functions having compact support contained in a sphere. Next, we demonstrate how our two main results give new insight into a reconstruction algorithm---based on the existing generalized sampling framework---that allows for stable and quasi-optimal reconstruction in any particular basis from a finite collection of samples. Finally, we construct sufficiently dense sampling schemes that are often used in practice---jittered, radial and spiral sampling schemes---and provide several examples illustrating the effectiveness of our approach when tested on these schemes

POCS-based reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE): A general algorithm for reducing motion-related artifacts.

PURPOSE: A projection onto convex sets reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE) is developed to reduce motion-related artifacts, including respiration artifacts in abdominal imaging and aliasing artifacts in interleaved diffusion-weighted imaging. THEORY: Images with reduced artifacts are reconstructed with an iterative projection onto convex sets (POCS) procedure that uses the coil sensitivity profile as a constraint. This method can be applied to data obtained with different pulse sequences and k-space trajectories. In addition, various constraints can be incorporated to stabilize the reconstruction of ill-conditioned matrices. METHODS: The POCSMUSE technique was applied to abdominal fast spin-echo imaging data, and its effectiveness in respiratory-triggered scans was evaluated. The POCSMUSE method was also applied to reduce aliasing artifacts due to shot-to-shot phase variations in interleaved diffusion-weighted imaging data corresponding to different k-space trajectories and matrix condition numbers. RESULTS: Experimental results show that the POCSMUSE technique can effectively reduce motion-related artifacts in data obtained with different pulse sequences, k-space trajectories and contrasts. CONCLUSION: POCSMUSE is a general post-processing algorithm for reduction of motion-related artifacts. It is compatible with different pulse sequences, and can also be used to further reduce residual artifacts in data produced by existing motion artifact reduction methods

Fast, Variable System Delay Correction for Spiral MRI

abstract: Magnetic Resonance Imaging using spiral trajectories has many advantages in speed, efficiency in data-acquistion and robustness to motion and flow related artifacts. The increase in sampling speed, however, requires high performance of the gradient system. Hardware inaccuracies from system delays and eddy currents can cause spatial and temporal distortions in the encoding gradient waveforms. This causes sampling discrepancies between the actual and the ideal k-space trajectory. Reconstruction assuming an ideal trajectory can result in shading and blurring artifacts in spiral images. Current methods to estimate such hardware errors require many modifications to the pulse sequence, phantom measurements or specialized hardware. This work presents a new method to estimate time-varying system delays for spiral-based trajectories. It requires a minor modification of a conventional stack-of-spirals sequence and analyzes data collected on three orthogonal cylinders. The method is fast, robust to off-resonance effects, requires no phantom measurements or specialized hardware and estimate variable system delays for the three gradient channels over the data-sampling period. The initial results are presented for acquired phantom and in-vivo data, which show a substantial reduction in the artifacts and improvement in the image quality.Dissertation/ThesisM.S. Bioengineering 201

Rosette Spectroscopic Imaging

Chemical shift imaging (CSI) has been the mainstay of spectroscopic imaging because of its simple implementation, reliability and ease of image reconstruction. This technique has been widely used for observing the changes in the metabolic signature of tissues during evolving pathological and/or physiological conditions. CSI owes its ease of implementation and analysis to the Fourier encoding approach upon which is based. In this approach, the spectral-spatial information is encoded in a rectilinear fashion that favors the acquisition of very high-resolution information along the spectral axis and relatively low resolution along the spatial directions. For applications where higher spatial resolution is desired over a narrower spectral bandwidth, trajectory designs that repeatedly cross the center of k-space through the use of time-dependent gradients offer a convenient means to achieve significant speedups in data acquisition. This stems from the fact that the readout period could be used to acquire multiple spatial frequency values which, in turn, leads to a reduction in the total number of RF excitations required to provide proper encoding of the spatial and spectral information. Among the trajectory designs that could be well suited for such a spectroscopic imaging approach the Rosette data acquisition approach is particularly attractive because of its relatively simple implementation and modest gradient requirements. The time-varying nature of the gradients in this trajectory design, while flexible, leads to smooth variations in sample density and larger signal bandwidths than those associated with the CSI gold standard. Despite these potential drawbacks, because no time is spent collecting information in the corners of k-space, we demonstrate that rosette spectroscopic imaging (RSI) can lead to an efficiency gain over CSI in a wide range of spectral bandwidths and spatial resolutions. An analytic relationship for the number of excitations to be used in an RSI experiment is derived and a method to obtain a more accurate self-derived B0 map that uses the information of the prevalent resonance in each voxel and linear regression is offered. Moreover, we show that any imaging technique that periodically samples the center and edges of k-space could be used for spectroscopic imaging