Automatic airway wall segmentation and thickness measurement for long-range optical coherence tomography images

被引：0
|
作者：
Qi, Li [1 ,3 ]
Huang, Shenghai [1 ]
Heidari, Andrew E. [1 ]
Dai, Cuixia [1 ]
Zhu, Jiang [1 ]
Zhang, Xuping
Chen, Zhongping [1 ,2 ]
机构：
[1] Univ Calif Irvine, Beckman Laser Inst, Irvine, CA 92612 USA
[2] Univ Calif Irvine, Dept Biomed Engn, Irvine, CA 92697 USA
[3] Nanjing Univ, Coll Engn & Appl Sci, Inst Opt Commun Engn, Nanjing 210093, Jiangsu, Peoples R China
来源：
OPTICAL COHERENCE TOMOGRAPHY AND COHERENCE DOMAIN OPTICAL METHODS IN BIOMEDICINE XX | 2016年 / 9697卷
关键词：
Optical Coherence Tomography; endoscopic imaging; image processing; SMOKE-INHALATION INJURY;
D O I：
10.1117/12.2214605
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
We present an automatic segmentation method for delineation and quantitative thickness measurement of multiple layers in endoscopic airway optical coherence tomography (OCT) images. The boundaries of the mucosa and the sub-mucosa layers were extracted using a graph-theory-based dynamic programming algorithm. The algorithm was tested with pig airway OCT images acquired with a custom built long range endoscopic OCT system. The performance of the algorithm was demonstrated by cross-validation between auto and manual segmentation experiments. Quantitative thicknesses changes in the mucosal layers are obtained automatically for smoke inhalation injury experiments.
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页数：7
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