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.

引用

页数：7

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