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A Diffusion Tensor Magnetic Resonance Image Tensor Domain Nonlocal Mean Denoising Method

A magnetic resonance image, non-local average technology, applied in image enhancement, image data processing, instruments, etc., can solve the problem of diffusion tensor magnetic resonance image noise influence and so on

Inactive Publication Date: 2017-01-25
CHENGDU UNIV OF INFORMATION TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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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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  • A Diffusion Tensor Magnetic Resonance Image Tensor Domain Nonlocal Mean Denoising Method

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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 non-local mean value denoising method in the tensor domain of a diffusion tensor magnetic resonance image, which belongs to the interdisciplinary technical field of digital image processing and applied mathematics, and solves the problem that the diffusion tensor magnetic resonance image is easily affected by noise. First, traverse the voxels of the diffusion tensor magnetic resonance image in turn, and set the corresponding search area centered on each voxel traversed; then perform tensor matrix similarity with all voxels in the search area one by one with the central voxel Finally, assign different weights to the voxels in the search area according to the similarity of the tensor matrix, calculate the weighted mean tensor matrix, and obtain the denoising result of the central voxel. The problem that the diffusion tensor magnetic resonance image is easily affected by 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 Patents(China)
IPC IPC(8): G06T5/00
Inventor 吴锡何嘉周激流
Owner CHENGDU UNIV OF INFORMATION TECH
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