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A Segmentation Method of CT Chest X-ray Lung Tissue Image Based on Level Set

An image segmentation and CT image technology, applied in the field of medical image processing, can solve the problems of easy omission of lung nodules, inaccurate segmentation boundaries, and dependence on segmentation templates, and achieve the effects of accurate segmentation results, high precision, and strong robustness.

Active Publication Date: 2022-03-04
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

All the above methods mainly use the grayscale information of the image to segment, so the boundary of the segmentation result will contain tissues other than the lungs, and it is easy to miss the pulmonary nodules located at the boundary of the segmentation result
[0004] The segmentation results of the existing methods are not robust enough, rely heavily on the segmentation template, the segmentation boundary is not precise enough, and the segmentation process requires manual intervention

Method used

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  • A Segmentation Method of CT Chest X-ray Lung Tissue Image Based on Level Set
  • A Segmentation Method of CT Chest X-ray Lung Tissue Image Based on Level Set
  • A Segmentation Method of CT Chest X-ray Lung Tissue Image Based on Level Set

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Embodiment 1

[0027] The results of the existing CT chest X-ray lung tissue image segmentation methods are not robust enough, rely heavily on the segmentation template, the segmentation boundary is not precise enough, and the segmentation process requires manual intervention. Aiming at the shortcomings of existing methods, a robust, fully automatic and accurate method for lung tissue image segmentation on CT chest X-rays is needed. For this reason the present invention proposes a kind of CT chest X-ray lung tissue image segmentation method based on level set, see figure 2 , including the following steps:

[0028] Step 1 Obtain a chest CT image containing lung tissue, use MATLAB to read the image into the computer, store the image in the computer as a three-dimensional tensor, and perform CT chest image preprocessing. The method of image preprocessing is to give the CT image grayscale Add 400 to the value, so that the distribution of the gray histogram of the original input CT image can be...

Embodiment 2

[0036] Level set-based CT chest X-ray lung tissue image segmentation method is the same as embodiment 1-1, see figure 2 , because the grayscale value dynamic range of the original input CT image is -1024 to +1024, and the image grayscale range of the statistical lung area is -600 to -200, so the image preprocessing described in step 1 is for CT Add 400 to the grayscale value of the image, so that the grayscale range of the image in the lung area can be adjusted to -200 to +200, making the lung area more prominent and convenient for subsequent processing. In order to reduce the complexity of the algorithm and reduce the interference of the image area other than the lung area, the gray value of the image is normalized to an 8-bit unsigned number.

Embodiment 3

[0038] The CT chest X-ray lung tissue image segmentation method based on the level set is the same as embodiment 1-2, and the energy functional equation is constructed on the preprocessed CT chest X-ray lung tissue image described in step 2 and an initial zero level set is set. , including the following steps:

[0039] 2.1 Construct the energy functional equation F(φ,c,b)

[0040] F(φ,c,b)=ε(φ,c,b)+υL(φ)+μR p (φ)

[0041] Where ε(φ,c,b) is the energy function, φ is the level set function, and c is the gray average vector inside and outside the level set, because CT images have different radioactivity levels, different imaging equipment, and different environment noise, so the bias field correction is introduced when constructing the energy functional equation. The bias field correction can reduce the influence of the above factors on image segmentation. b is the bias field parameter. At the same time, when constructing the energy functional equation The zero level set arc l...

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Abstract

The invention discloses a level set-based method for segmenting lung tissue images of CT chest X-rays, which solves the problems of inaccurate lung region segmentation boundaries, manual intervention and unstable results in CT images. The implementation process includes: obtaining CT images and preprocessing; constructing energy functionals and setting initial zero level sets; minimizing energy functionals to obtain zero level set contours; selecting the contours of candidate lung regions; Area filling contour; perform morphological opening and closing operations on the filling results, and remove small-volume connected areas. The invention extracts the edge information of the image, designs a stable contour screening strategy based on prior knowledge, effectively screens out the contours of candidate lung regions, and finally designs an optimization scheme for contour filling based on contour height information. The image segmentation result of the invention is robust and the precision is high, and it is a fully automatic image segmentation method. The invention extracts the lung area of ​​the CT image, which can be used for subsequent analysis of the lung area of ​​the CT image.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to lung image segmentation, in particular to a level set-based CT chest X-ray lung tissue image segmentation method, which is used in lung CT image processing. Background technique [0002] In recent years, with the continuous improvement of computed tomography imaging technology, high-definition, high-contrast CT images can be provided for the diagnosis of human diseases. Observing the structural and functional characteristics of the lungs using CT images of the lungs is an important clinical diagnostic method for lung diseases today. At the same time, due to the enhancement of computer computing power, deep learning technology has achieved breakthrough results in the field of computer vision. Naturally, computer-based automatic detection of pulmonary nodules has also been developed. The original lung CT image contains a large amount of background, muscle, fat, p...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/12G06T7/13G06T7/62
CPCG06T7/12G06T7/13G06T7/62G06T2207/30061G06T2207/20036G06T2207/10081
Inventor 王蓉芳杨靖陈佳伟郝红侠缑水平刘红英
Owner XIDIAN UNIV
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