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Proceedings Paper

Accurate airway segmentation based on intensity structure analysis and graph-cut
Author(s): Qier Meng; Takayuki Kitsaka; Yukitaka Nimura; Masahiro Oda; Kensaku Mori
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Paper Abstract

This paper presents a novel airway segmentation method based on intensity structure analysis and graph-cut. 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 a complete 3-D airway tree structure from a CT volume is quite challenging. Several researchers have proposed automated algorithms basically based on region growing and machine learning techniques. However these methods failed to detect the peripheral bronchi branches. They caused a large amount of leakage. This paper presents a novel approach that permits more accurate extraction of complex bronchial airway region. Our method are composed of three steps. First, the Hessian analysis is utilized for enhancing the line-like structure in CT volumes, then a multiscale cavity-enhancement filter is employed to detect the cavity-like structure from the previous enhanced result. In the second step, we utilize the support vector machine (SVM) to construct a classifier for removing the FP regions generated. Finally, the graph-cut algorithm is utilized to connect all of the candidate voxels to form an integrated airway tree. We applied this method to sixteen cases of 3D chest CT volumes. The results showed that the branch detection rate of this method can reach about 77.7% without leaking into the lung parenchyma areas.

Paper Details

Date Published: 21 March 2016
PDF: 9 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842G (21 March 2016); doi: 10.1117/12.2216670
Show Author Affiliations
Qier Meng, Nagoya Univ. (Japan)
Takayuki Kitsaka, Aichi Institute of Technology (Japan)
Yukitaka Nimura, Nagoya Univ. (Japan)
Masahiro Oda, Nagoya Univ. (Japan)
Kensaku Mori, Nagoya Univ. (Japan)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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