Segmentation of the airways is useful for the analysis of airway compression and obstruction caused by pathology. This paper outlines an automatic method for segmentation of the airway tree. This method includes algorithms to detect the trachea, segment the trachea and main bronchi by thresholding and region growing, and segment the remaining bronchi by morphological filtering and reconstructio...

This paper presents a method for airway tree segmentation that uses a combination of a trained airway appearance model, vessel and airway orientation information, and region growing. The method uses a voxel classification based appearance model, which involves the use of a classifier that is trained to differentiate between airway and non-airway voxels. Vessel and airway orientation information...

This paper presents a voxel classification based method for segmenting the human airway tree in volumetric computed tomography (CT) images. In contrast to standard methods that use only voxel intensities, our method uses a more complex appearance model based on a set of local image appearance features and K nearest neighbor (KNN) classification. The optimal set of features for classification is...

Analysis of vascular and airway trees of circulatory and respiratory systems is important for a wide range of clinical applications. Automatic segmentation of these tree-like structures from 3D image data remains challenging due to complex branching patterns, geometrical diversity, and pathology. Existing automated techniques are sensitive to parameters setting, may leak into nearby structures,...

Analysis of vascular and airway trees of circulatory and respiratory systems is important for many clinical applications. Automatic segmentation of these tree-like structures from 3D data remains an open problem due to their complex branching patterns, geometrical diversity, and pathology. On the other hand, it is challenging to design intuitive interactive methods that are practical to use in ...

This paper presents a method for airway tree segmentation that uses a combination of a trained airway appearance model, vessel and airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between airway and non-airway voxels. This is in contrast to previous works that use eit...

The airway tree is one of the most important part in human respiratory system. Airway segmentation plays a crucial role pulmonary disease diagnosis, localization and surgical navigation. We propose novel method to improve thoracic computed tomography(CT) using deep learning. In order take into account multi-scale changes achieve accurate segmentation, we design an end-to-end Tiny Atrous Convolu...

Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in segmentation, particularly the small branches airway. These difficulties arise due to constraints limited labeling failure meet clinical use r...

Human airway tree segmentation from computed tomography (CT) images is a very important step for virtual bronchoscopic applications. Imaging artifacts or thin airway walls decrease the contrast between the air and airway wall and make the segmented region to leak from inside of the airway to the parenchyma. This in turn begins the leakage phenomenon to build and then large parts of the lung par...

The segmentation of the airway tree is an important preliminary step for many clinical applications. In this paper we present a method for fully automated extraction of airways from volumetric computed tomography (CT) images based on a self-adapting region growing process. The method consists of 3 main steps. Firstly the histogram of a dataset is analysed. Secondly the trachea is searched and s...