When applying 2D deformable registration to cross-sectional images from 3D data, it is assumed the images represent the same object. However, in reality, they are acquired at different axial positions and with differe...
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When applying 2D deformable registration to cross-sectional images from 3D data, it is assumed the images represent the same object. However, in reality, they are acquired at different axial positions and with different coronal and sagittal rotations. Therefore, if the cross-section's position and orientation can be corrected, it should offer better registration accuracy. The paper describes a "2.5D" method for correcting position and orientation of the cross-section under the in-plane-2D deformable registration. For a given source image, the algorithm estimates the optimal position and orientation of the cross-section generating target image as well as displacement field of the source image. For in-plane 2D deformable registration, a previously proposed method is being used. It models the deformations using a spring mass system, which contains a number of sparse masses interconnected by springs. The external forces calculated from the image contents are used to iteratively guide the system to the "good" matching configuration under the constraints imposed by the internal forces. The performance of the method is tested using real Radiation Therapy Planning CT (RTPCT) and Cone Beam CT (CBCT) as well as the simulated data. The results indicate that the newly proposed method improves registration accuracy.
The paper describes a novel multi-resolution registration method. It is fast, robust and offers high registration accuracy. The algorithm models deformations using an elastic spring mass system, which contains sparse ...
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ISBN:
(纸本)1904410146
The paper describes a novel multi-resolution registration method. It is fast, robust and offers high registration accuracy. The algorithm models deformations using an elastic spring mass system, which contains sparse masses interconnected by springs. The proposed method uses data intensity values to guide deformation with local constraints imposed by interaction of interconnecting springs. Moreover, by using such system prior information about the data can by easily embedded into the system to improve the registration accuracy. The performance of the method is tested using simulated as well as real dynamic magnetic resonance image dMRI data.
The paper describes a novel deformable data registration algorithm. The proposed method can be seen as a tradeoff between the landmark and intensity driven data registration techniques. The algorithm is fast, robust a...
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The paper describes a novel deformable data registration algorithm. The proposed method can be seen as a tradeoff between the landmark and intensity driven data registration techniques. The algorithm is fast, robust a...
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The paper describes a novel deformable data registration algorithm. The proposed method can be seen as a tradeoff between the landmark and intensity driven data registration techniques. The algorithm is fast, robust and offers high registration accuracy. The algorithm enables to include complex constraints on the allowable data deformations. It is shown that some of these constraints permit to treat part of the data as rigid objects or restrict its deformation to a predefined shape. The main focus of the paper is put on the description of the elastic deformation model used in the method. The performance of this new technique is illustrated using simulated as well as real computed tomography (CT) images.
The emergence of HDTV accelerates the evolution of high-resolution imaging systems. A 3CCD digital camera system has been developed for higher resolution than one CCD imaging has. From the pixel correlation caused by ...
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ISBN:
(纸本)0780388747
The emergence of HDTV accelerates the evolution of high-resolution imaging systems. A 3CCD digital camera system has been developed for higher resolution than one CCD imaging has. From the pixel correlation caused by a half-pixel shift of the green channel, we can interpolate pixels and get four times higher resolution of the color image. The proposed method involves three projection operators. The first is to reduce aliasing of image regions by selective projection in subband channels. The second projection makes an inverse of the MTF which generates blurring over the entire image. The last operator works for fast convergence. From experimental results, the proposed algorithm shows suppression of jagging effects and restoration of aliased image regions. It is experimentally shown that the projection process converges and is almost finished at the first iteration.
This paper presents a color demosaicing method by introducing iterative asymmetric average interpolation. Missing primary colors on a Bayer pattern color filter array (CFA) are estimated by an asymmetric average inter...
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We have developed a new signal-dependent representation which captures, with a few KL vectors and transform coefficients, the perceptually and phonetically important structure of the spectral envelope. Together with a...
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