Airway extraction from 3D chest CT volumes based on iterative extension of VOI enhanced by cavity enhancement filter

Qier Meng; Takayuki Kitasaka; Masahiro Oda; Kensaku Mori

doi:10.1117/12.2254233

3 March 2017 Airway extraction from 3D chest CT volumes based on iterative extension of VOI enhanced by cavity enhancement filter

Qier Meng, Takayuki Kitasaka, Masahiro Oda, Kensaku Mori

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101343S (2017) https://doi.org/10.1117/12.2254233
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Airway segmentation is an important step in analyzing chest CT volumes for computerized lung cancer detection, emphysema diagnosis, asthma diagnosis, and pre- and intra-operative bronchoscope navigation. However, obtaining an integrated 3-D airway tree structure from a CT volume is a quite challenging task. This paper presents a novel airway segmentation method based on intensity structure analysis and bronchi shape structure analysis in volume of interest (VOI). This method segments the bronchial regions by applying the cavity enhancement filter (CEF) to trace the bronchial tree structure from the trachea. It uses the CEF in each VOI to segment each branch and to predict the positions of VOIs which envelope the bronchial regions in next level. At the same time, a leakage detection is performed to avoid the leakage by analysing the pixel information and the shape information of airway candidate regions extracted in the VOI. Bronchial regions are finally obtained by unifying the extracted airway regions. The experiments results showed that the proposed method can extract most of the bronchial region in each VOI and led good results of the airway segmentation.

Citation Download Citation

Qier Meng, Takayuki Kitasaka, Masahiro Oda, and Kensaku Mori "Airway extraction from 3D chest CT volumes based on iterative extension of VOI enhanced by cavity enhancement filter", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343S (3 March 2017); https://doi.org/10.1117/12.2254233

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