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Low dose CT image filtering method

A CT image, low-dose technology, applied in the field of medical image processing, can solve the problems of severe star-stripe artifacts, attenuation, and inability to effectively suppress star-stripe artifacts, etc., and achieve the effect of improving the quality of CT images

Inactive Publication Date: 2013-07-31
SOUTHEAST UNIV
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Problems solved by technology

[0008] The WIA-LN method has proved its processing effect in low-dose abdominal CT images, and it can still obtain better image restoration effect in the abdominal CT image processing under the condition that the tube current is reduced to one-fifth of the original one. Considering The linear relationship between dose and tube current, the WIA-LN denoising method can reduce the dose received by patients to one-fifth of the original in abdominal scans, but this method has certain limitations, and it is easy to reduce the dose under low-dose conditions. The star-and-stripe artifacts in CT images are used as information in the image, so it is impossible to effectively suppress the star-stripe artifacts that are prone to appear in CT images under low-dose scanning conditions. These star-stripe artifacts are caused by human tissue There is a large attenuation of X-ray projections from certain angles, which generally appear in CT scan images of parts with more high density (such as bones), such as shoulders, chest and spine
Under low-dose conditions with reduced tube current or voltage, the star-streaked artifacts will be more serious due to the penetrability and energy drop of X-rays

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Embodiment Construction

[0024] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0025] The idea of ​​the present invention is to organically combine the two strategies of dictionary learning denoising and unsharp filtering for low-dose CT images that are difficult to effectively remove with existing methods. The blocky noise in the original low-dose CT image is removed, and then the suppressed edge information is enhanced by the unsharp filtering method.

[0026] The specific method of this method is as follows:

[0027] Utilize the dictionary learning method to filter out the blocky noise in the original low-dose CT image, specifically adopt the following methods in this embodiment:

[0028] 1) Use discrete cosine transform (Discrete Cosine Transform, referred to as DCT transform) to generate a DCT dictionary, the method is as follows: given sequence x(n), n=0,1,...N 2 -1, its discrete cosine transform:

[0029] ...

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Abstract

The invention discloses a low dose CT (Computerized Tomography) image filtering method, and belongs to the technical field of medical image processing. The method comprises the steps that a dictionary learning method is used for filtering block noise in the original low dose CT image, and an obtained image is subjected to unsharp filtering for enhancing image border information inhibited by the dictionary learning method. The method can effectively inhibit star strip artifacts and noise in the low dose CT image, and improve the quality of the CT image.

Description

technical field [0001] The invention relates to an image filtering method, in particular to a low-dose CT image filtering method, and belongs to the technical field of medical image processing. Background technique [0002] As a conventional and effective clinical medical diagnostic tool at present, X-ray computerized tomography (X-ray Computerized Tomography, CT) can clearly obtain the attenuation information of different tissues of the human body for X-rays on the millimeter scale, so as to provide clinicians with diagnostic tools. and prevention provide rich 3D human organ tissue information. Today, as a mature and clinically recognized inspection method, CT has become one of the indispensable and main tools in the field of radiological diagnosis. However, with the popularity of CT tomography in clinical diagnosis, especially in routine examinations, the radiation dose in CT scanning has attracted more and more attention. A large number of clinical studies have shown tha...

Claims

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Application Information

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IPC IPC(8): G06T5/00A61B6/03
Inventor 陈阳余飞罗立民李松毅鲍旭东
Owner SOUTHEAST UNIV
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