High-accuracy rotation of images

Danielsson, Per-Erik; Hammerin, Magnus

doi:10.1016/1049-9652(92)90080-h

Cited by 56 publications

(45 citation statements)

References 2 publications

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“…A frequently used alternative approach to study the performance of interpolation kernels for the purpose of applying geometrical transformations, is to apply these transfor-mations to a number of test-images, followed by the inverse transformation so as to bring the images back in their original position [6,13,28,33,38]. Ideally, the forward-backward transformed images should be identical to their respective originals, so that a quantitative performance measure can be based on the grey-value differences between the images.…”

Section: Discussion Of Evaluation Strategiesmentioning

confidence: 99%

“…In the evaluation, we restricted ourselves to the class of kernels for which n and m are related by n = 2m − 1. This is a rather broad class, which includes the linear interpolation kernel (6) and all of the Lagrange interpolation kernels described in the previous subsection. It also includes the quadratic piecewise polynomial kernel due to Dodgson [7].…”

Section: Generalized Convolution Kernelsmentioning

confidence: 99%

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Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

254

165

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Abstract-Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations (rotations and translations). A large number of sincapproximating kernels are evaluated, including piecewise polynomial kernels and a large number of windowed sinc kernels, with spatial supports ranging from two to ten grid intervals. In the evaluation we use images from a wide variety of medical image modalities. The results show that spline interpolation is to be preferred over all other methods, both for its accuracy and its relatively low computational cost.

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Section: Discussion Of Evaluation Strategiesmentioning

confidence: 99%

Section: Generalized Convolution Kernelsmentioning

confidence: 99%

Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

254

165

View full text Add to dashboard Cite

show abstract

“…For our system, we chose the latter solution of a hardware rotator. Different approaches for the rotation algorithm have been investigated, rotation using distortion matrices [6], trilinear interpolation [5], and Gaussian interpolation masks [15]. For the last method, the gray value of the voxel v in the rotated coordinate system is interpolated by the gray values of its 8 neighbors v r ' in the original coordinate system.…”

Section: Lutmentioning

confidence: 99%

Virim: A massively parallel processor for real-time volume visualization in medicine

Günther

Poliwoda

Reinhart

et al. 1995

Computers & Graphics

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Architecture and applications of a massively parallel processor are described. Volumes of 256×256×128 voxels can be visualized at a frame rate of 10 Hz using volume oriented visualization algorithms. A prototype of the scalable and modular system is currently set up. 3D rotation around an arbitrary rotation axis, perspective, zooming, and arbitrary gray value mapping are provided in real-time. Multi-user access over high-speed networks is possible. A volume oriented visualization algorithm is used that is tailored to the requirements in medicine [5]. With this algorithm, small structures of a size down to the pixel resolution, and structures without defined surfaces can be visualized as well as semi-transparent objects. One application of the system is therapy planning in heart surgery.

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“…A threeshear decomposition of a 2D image rotation was introduced independently by Paeth [11] and Tanaka et al [15]. A straightforward extension of this method to 3D was proposed by Schroeder and Salem [14], also by Danielsson and Hammerin [4]. From the initially obtained nine shear decomposition sequence, they managed to merge two neighboring shears into a single shear, resulting in an eight-shear decomposition.…”

Section: Introductionmentioning

confidence: 99%

“…From the initially obtained nine shear decomposition sequence, they managed to merge two neighboring shears into a single shear, resulting in an eight-shear decomposition. Schroeder and Salem [4] have implemented the eight-pass rotation on CM-2. The first attempt of directly performing decomposition on 3D rotation was taken by Wittenbrink and Somani [19] and recently by Toffoli and Quick [16].…”

Section: Introductionmentioning

confidence: 99%

Two-Pass Image and Volume Rotation

Chen

Kaufman

2001

Eurographics

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We present a novel two-pass approach for both 2D image and 3D volume rotation. Each pass is a pseudo shear. However, it has the similar regularity to a pure shear in that a beam remains rigid. Furthermore, the 3D pseudo shear guarantees that beams within one major axis slice remain in the same directional plane after the shearing. These properties make it feasible to implement the pseudo shears on a multi-pipelined hardware or a massively parallel machine. Compared with the existing decompositions, ours offer a minimum number of shears to realize an arbitrary 3D rotation. Our decomposition also preserves the image/volume quality by guaranteeing no minification for the first pass shear.

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High-accuracy rotation of images

Cited by 56 publications

References 2 publications

Quantitative evaluation of convolution-based methods for medical image interpolation

Quantitative evaluation of convolution-based methods for medical image interpolation

Virim: A massively parallel processor for real-time volume visualization in medicine

Two-Pass Image and Volume Rotation

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