title: Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.
abstract: OBJECTIVE
  Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree.
  
  
  METHODS
  This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree.
  
  
  RESULTS
  A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate.
  
  
  CONCLUSIONS
  A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.
authors: Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan. qmeng@mori.m.is.nagoya-u.ac.jp.
authors: Qier
authors: Q
authors: Meng
authors: Graduate School of Information Science, Aichi Institute of Technology, 1247 Yachigusa, Yagusa-cho, Toyota, Aichi, Japan.
authors: Takayuki
authors: T
authors: Kitasaka
authors: Information Strategy Office, Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan.
authors: Yukitaka
authors: Y
authors: Nimura
authors: Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan.
authors: Masahiro
authors: M
authors: Oda
authors: Department of Diagnostic Radiology, Graduate School of Health Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8509, Japan.
authors: Junji
authors: J
authors: Ueno
authors: Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan.|Information Strategy Office, Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan.
authors: Kensaku
authors: K
authors: Mori
mesh_terms: D000465:Algorithms; D001331:Automation; D001980:Bronchi / Q000000981:diagnostic imaging*; D001999:Bronchoscopy; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D009929:Organ Size; D060388:Support Vector Machine; D013909:Thorax; D014057:Tomography, X-Ray Computed / Q000379:methods
keywords: Adaptive multiscale cavity enhancement filter; Graph-cut; Hessian matrix analysis; Support vector machine