Automated lung segmentation algorithm for CAD system of thoracic CT

Cao Lei1; Li Xiaojian2; Zhan Jie1; Chen Wufan1 1Department of Information Technology; Biomedical Engineering School; Southern Medical University; Guangzhou 510515; China 2Department of Plastic Surgery; Nanfang Hospital; Southern Medical University; Guangzhou 510515; China

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Automated lung segmentation algorithm for CAD system of thoracic CT

Updated：2026-03-12

- Automated lung segmentation algorithm for CAD system of thoracic CT
- Automated lung segmentation algorithm for CAD system of thoracic CT
- 解放军医学杂志（英文版） 2008年第4期页码：215-222
- Affiliations：
- Author bio：
- Funds：
  
  the National Key Basic Research and Development Plan of China （“973” Projects, 2003CB716104）;the Key Program of the National Natural Science Foundation of China （30730036）;the Sci & Tech Planning Program of Guangdong Province （2007B010400058）;the Sci & Tech Project Foundation of Guangzhou City （2007Z3-E0031）
- DOI：
  中图分类号： R734.2
- 纸质出版：2008
- Accepted：
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Automated lung segmentation algorithm for CAD system of thoracic CT[J]. 解放军医学杂志（英文版）, 2008,(4):215-222.

[1].Automated lung segmentation algorithm for CAD system of thoracic CT[J].Journal of Medical Colleges of PLA,2008(04):215-222.
Automated lung segmentation algorithm for CAD system of thoracic CT[J]. 解放军医学杂志（英文版）, 2008,(4):215-222. DOI：

[1].Automated lung segmentation algorithm for CAD system of thoracic CT[J].Journal of Medical Colleges of PLA,2008(04):215-222. DOI：

摘要

Abstract

Objective: To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic （CAD） system

especially on the segmentation between left and right lungs. Methods: We put forward the base frame of our lung segmentation firstly. Then

using optimal thresholding and mathematical morphologic methods

we acquired the rough image of lung segmentation. Finally

we presented a fast self-fit segmentation refinement algorithm

adapting to the unsuccessful left-right lung segmentation of thredsholding. Then our algorithm was used to CT scan images of 30 patients and the results were compared with those made by experts. Results: Experiments on clinical 2-D pulmonary images showed the results of our algorithm were very close to the expert’s manual outlines

and it was very effective for the separation of left and right lungs with a successful segmentation ratio 94.8%. Conclusion: It is a practicable fast lung segmentation algorithm for CAD system on thoracic CT image.

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