Application of Image Processing and 3D Printing Technique to Development of Computer Tomography System for Automatic Segmentation and Quantitative Analysis of Pulmonary Bronchus

Chung Feng Jeffrey Kuo, Zheng-Xun Yang, Wen-Sen Lai and Shao-Cheng Liu ()
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Chung Feng Jeffrey Kuo: Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
Zheng-Xun Yang: Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
Wen-Sen Lai: Department of Otolaryngology-Head and Neck Surgery, Taichung Armed Forces General Hospital, Taichung 411, Taiwan
Shao-Cheng Liu: Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan

Mathematics, 2022, vol. 10, issue 18, 1-25

Abstract: This study deals with the development of a computer tomography (CT) system for automatic segmentation and quantitative analysis of the pulmonary bronchus. It includes three parts. Part I employed an adaptive median and four neighbors low pass filters to eliminate the noise of CT. Then, k-means clustering was used to segment the lung region in the CT data. In Part II, the pulmonary airway was segmented. The three-grade segmentation was employed to divide all pixels in the lung region into three uncertain grades, including air, blood vessels, and tissues, and uncertain portions. The airway wall was reformed using a border pixel weight mask. Afterwards, the seed was calculated automatically with the front-end image masking the aggregation position of the lung region as the input of the region growing to obtain the initial airway. Afterwards, the micro bronchi with different radii were detected using morphological grayscale reconstruction to modify the initial airway. Part III adopted skeletonization to simplify the pulmonary airway, keeping the length and extension direction information. The information was recorded in a linked list with the world coordinates based on the patients’ carina, defined by the directions of the carina to the top end of the trachea and right and left main bronchi. The whole set of bronchi was recognized by matching the target bronchus direction and world coordinates using hierarchical classification. The proposed system could detect the location of the pulmonary airway and detect 11 generations’ bronchi with a bronchus recognition capability of 98.33%. Meanwhile, 20 airway parameters’ measurement and 3D printing verification have been processed. The diameter, length, volume, angle, and cross-sectional area of the main trachea and the right and left bronchi, the cross-sectional area of the junction, the left bronchus length, and the right bronchus length have been calculated for clinical practice guidelines. The system proposed in this study simultaneously maintained the advantages of automation and high accuracy and contributed to clinical diagnosis.

Keywords: bronchus; image processing; computer-aided detection; k-means; 3D reconstruction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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