The dipole expansion method for plasma simulation

Kruer, W. L.; Dawson, J. M.; Rosen, Bernard

doi:10.1016/0021-9991(73)90129-0

Cited by 63 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since fluid elements are constrained on grids, one simple way to achieve this is to linearly interpolate the charge Q into adjacent discrete points. Considering the interpolation accuracy as well as the computational cost, we use the SUDS technique, which is the accelerated version 31 of the DEM, to interpolate the charge to the nearest grid point and its 4-neighbors while achieving the conservation of charge, as shown in Fig. 4͑b͒.…”

Section: Charge Interpolationmentioning

confidence: 99%

Image segmentation using a charged fluid method

Valentino

2006

J. Electron. Imaging

View full text Add to dashboard Cite

Section: Charge Interpolationmentioning

confidence: 99%

Image segmentation using a charged fluid method

Valentino

2006

J. Electron. Imaging

View full text Add to dashboard Cite

“…This gives us a dipole expansion approximation and replaces the sum over particles by a sum over grid points as [22], [24] where (x g , y g ) is the NGP location. Finally, let us approximate the derivatives of using the central difference method over the adjacent grids (15) where Q NGP is the monopole charge contribution and D(ξ g ) is the dipole charge contributions Using this approximation based upon (15), we can obtain the charge density by summing the charge over the corresponding five neighboring grids on each fluid element.…”

Section: A Sudsmentioning

confidence: 99%

“…We do this by interpolating the charge in Fig. 3(a) to the nearest grid point (NGP) and its 4-neighbors using the subtracted dipole scheme (SUDS) [22] associated with the FSP technique [23], [24], which is described in Section III-A.…”

Section: A Charge Densitymentioning

confidence: 99%

See 1 more Smart Citation

Segmentation of Brain MR Images Using a Charged Fluid Model

Chang

Valentino

Duckwiler

et al. 2007

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

In this paper, we developed a new deformable model, the charged fluid model (CFM), that uses the simulation of a charged fluid to segment anatomic structures in magnetic resonance (MR) images of the brain. Conceptually, the charged fluid behaves like a liquid such that it flows through and around different obstacles. The simulation evolves in two steps governed by Poisson's equation. The first step distributes the elements of the charged fluid within the propagating interface until an electrostatic equilibrium is achieved. The second step advances the propagating front of the charged fluid such that it deforms into a new shape in response to the image gradient. This approach required no prior knowledge of anatomic structures, required the use of only one parameter, and provided subpixel precision in the region of interest. We demonstrated the performance of this new algorithm in the segmentation of anatomic structures on simulated and real brain MR images of different subjects. The CFM was compared to the levelset-based methods [Caselles et al. (1993) and Malladi et al. (1995)] in segmenting difficult objects in a variety of brain MR images. The experimental results in different types of MR images indicate that the CFM algorithm achieves good segmentation results and is of potential value in brain image processing applications.

show abstract

“…To obtain the discrete analog to the V 2 operator we use the 3-point two dimensional difference, that is, since The weighting scheme chosen in 1D may be interpreted as a dipole expansion of the charge density about the midpoint between cells [29]. In two dimensions a similar approach, but including a quadrupole term yields a scheme known as area weighting [5, p. 244].…”

Section: Difference Equationsmentioning

confidence: 99%