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.
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.