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Tracheal tree full-automatic segmentation method based on differential geometry

A differential geometry, tracheal tree technology, applied in image analysis, image data processing, instruments, etc., to achieve the effect of easy implementation and insensitivity to noise images

Pending Publication Date: 2020-05-08
浙江影加医疗设备有限公司
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Problems solved by technology

[0005] In order to solve the above-mentioned existing problems, the present invention adopts a fully automatic tracheal tree segmentation method based on differential geometry, which is fully automatic, easy to implement, insensitive to noise images, and the segmented tracheal tree has no leakage and blockage problems

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  • Tracheal tree full-automatic segmentation method based on differential geometry
  • Tracheal tree full-automatic segmentation method based on differential geometry
  • Tracheal tree full-automatic segmentation method based on differential geometry

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Embodiment Construction

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0030] The technical scheme adopted by the present invention is as follows: a method for fully automatic tracheal tree segmentation based on differential geometry, including the following steps:

[0031] 1) Surface modeling of lung CT: equidistant sampling with a sampling interval of 10HU for the gray value range [-900HU, -450HU], the CT value of the tracheal tree at different positions varies greatly, [-900HU, -450HU ] The gray value range can cover all the CT values ​​of tracheal trees; according to the adopted CT values, the...

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Abstract

The invention discloses a tracheal tree full-automatic segmentation method based on differential geometry. The tracheal tree full-automatic segmentation method comprises the following steps: 1) carrying out surface modeling of lung CT; 2) calculating a main curvature and a main direction; 3) filtering a non-tracheal area; 4) removing a non-tracheal region; 5) extracting a connected region; 6) removing connected regions with planar shape characteristics; 7) fusing the tracheal tree sets S generated by different CT values, and 8) obtaining an organ tree surface model. The invention belongs to the technical field of tracheal tree full-automatic segmentation, and particularly relates to a tracheal tree full-automatic segmentation method based on differential geometry, which is full-automatic,easy to implement and insensitive to noise images, and segmented tracheal trees are free of leakage and blockage.

Description

Technical field [0001] The invention belongs to the technical field of automatic tracheal tree segmentation, and specifically refers to a method for automatic tracheal tree segmentation based on differential geometry. Background technique [0002] The precise segmentation and quantitative analysis of bronchial tree morphology play a very important role in clinical evaluation of the existence and severity of specific diseases (for example: bronchial lumen area reduction, wall thickness thinning, etc.). The current computed tomography CT imaging technology can Realize the visualization of 3D lung structure, but cannot provide separate display and quantitative description of the tracheal tree. Due to the complex structure of the tracheal tree, manual segmentation is very time-consuming and labor-intensive. At the same time, due to the subjective evaluation of the operator, large manual errors are usually introduced Therefore, an automatic and reliable tracheal tree segmentation meth...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/62
CPCG06T7/0012G06T7/11G06T7/62G06T2207/10081G06T2207/30101
Inventor 罗哲
Owner 浙江影加医疗设备有限公司
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