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Dispersion tensor magnetic resonance image tensor domain non-local mean denoising method

A magnetic resonance image, non-local mean technology, applied in image enhancement, image data processing, instruments, etc., can solve the problem of noise effects of diffusion tensor magnetic resonance images

Inactive Publication Date: 2014-08-13
CHENGDU UNIV OF INFORMATION TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the shortcomings of the prior art, the present invention provides a non-local mean value denoising method in the tensor domain of a diffusion tensor magnetic resonance image. A comprehensive comparison of the geometric structure and spatial direction similarity of the three-dimensional tensor model, assigning high weights to high similarity voxels and performing weighted mean method to obtain tensor data after denoising, which solves the problem of difficulty in diffusion tensor MRI images Problems Affected by Noise

Method used

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Embodiment 1

[0047] Step 1: In the background area, calculate the grayscale histogram of the background area, and use the Gaussian function to fit the grayscale histogram, determine the noise standard deviation according to the variance of the Gaussian function, and set the attenuation coefficient h to 1.2 times the noise standard deviation , the search area radius Ω is 10% of the larger value of the entire image length compared to its width.

[0048]Step 2: According to the noise standard deviation, attenuation coefficient h and search area radius Ω, traverse all voxels in the diffusion tensor magnetic resonance image dataset with a resolution of 128 rows, 128 columns and 53 layers in turn, and use each voxel traversed As the center, set a square search area Q with a radius of Ω, and the square search area Q is a cube with 13 rows, 13 columns and 13 floors.

[0049] Step 3: Compare all voxels in the square search area Q with the central voxel in turn for tensor matrix similarity, and use ...

Embodiment 2

[0062] Step 1: In the background area, calculate the grayscale histogram of the background area, and use the Gaussian function to fit the grayscale histogram, determine the noise standard deviation according to the variance of the Gaussian function, and set the attenuation coefficient h to 1.2 times the noise standard deviation , the search area radius Ω is 10% of the larger value of the entire image length compared to its width.

[0063] Step 2: According to the noise standard deviation, attenuation coefficient h and search area radius Ω, traverse all voxels in the diffusion tensor magnetic resonance image dataset with a resolution of 128 rows, 128 columns and 53 layers in turn, and use each voxel traversed As the center, set a square search area Q with a radius of Ω, and the square search area Q is a cube with 13 rows, 13 columns and 13 floors.

[0064] Step 3: Compare all voxels in the square search area Q with the central voxel in turn for tensor matrix similarity, and use...

Embodiment 3

[0076] Step 1: In the background area, calculate the grayscale histogram of the background area, and use the Gaussian function to fit the grayscale histogram, determine the noise standard deviation according to the variance of the Gaussian function, and set the attenuation coefficient h to 1.2 times the noise standard deviation , the search area radius Ω is 10% of the larger value of the entire image length compared to its width.

[0077] Step 2: According to the noise standard deviation, attenuation coefficient h and search area radius Ω, traverse all voxels in the diffusion tensor magnetic resonance image dataset with a resolution of 128 rows, 128 columns and 53 layers in turn, and use each voxel traversed As the center, set a square search area Q with a radius of Ω, and the square search area Q is a cube with 13 rows, 13 columns and 13 floors.

[0078] Step 3: Compare all voxels in the square search area Q with the central voxel in turn for tensor matrix similarity, and use...

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Abstract

The invention discloses a dispersion tensor magnetic resonance image tensor domain non-local mean denoising method, and belongs to the technical field of digital image processing and applied mathematics interdisciplines. The problem that a dispersion tensor magnetic resonance image is easily affected by noise is solved. The method comprises the steps of firstly, sequentially conducting traversal on voxels of the dispersion tensor magnetic resonance image, setting a corresponding search region by using each voxel obtained by traversal as the center, then, conducting tensor matrix similarity comparison between all the voxels inside the search region and the center voxel, finally giving different weights to the voxels inside the search region according to the degree of the tensor matrix similarity, calculating a weighted mean tensor matrix, and obtaining the denoising result of the center voxel. The problem that the dispersion tensor magnetic resonance image is easily affected by the noise is solved.

Description

technical field [0001] The invention discloses a non-local mean value denoising method in the tensor domain of a diffusion tensor magnetic resonance image, which is used for the denoising of the diffusion tensor magnetic resonance image, and belongs to the interdisciplinary technical field of digital image processing and applied mathematics. Background technique [0002] Diffusion magnetic resonance imaging is an imaging tool for non-invasive research on the structure and physiological function of living brain tissue. It can be used to directly evaluate the physiological function of living brain fiber tissue, and use fiber imaging technology to indirectly estimate and reconstruct the three-dimensional structure of brain nerve fibers. structure. Diffusion magnetic resonance imaging has been deeply applied in the study of the central nervous system, and can be extended to other human fibrous tissues. It has great potential in the fields of psychology, cognition, and clinical m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 吴锡何嘉周激流
Owner CHENGDU UNIV OF INFORMATION TECH
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