Modern multislice X-ray CT scanners provide high-resolution volumetric image data containing a wealth of structural and functional information. The size of the volumes makes it more and more difficult for human observers to visually evaluate their contents. Similar to other areas of medical image analysis, highly automated extraction and quantitative assessment of volumetric data is increasingl...

We present a pulmonary vessel segmentation algorithm, which is fast, fully automatic and robust. It uses a coarse segmentation of the airway tree and a left and right lung labeled volume to restrict a vessel enhancement filter, based on an offset medialness function, to the lungs. We show the application of our algorithm on contrast-enhanced CT images, where we derive a clinical parameter to de...

The quantitative analysis of the airway tree is of critical importance in the CT-based diagnosis and treatment of popular pulmonary diseases. The extraction of airway centerline is a precursor to identify airway hierarchical structure, measure geometrical parameters, and guide visualized detection. Traditional methods suffer from extra branches and circles due to incomplete segmentation results...

The use of computers in facilitating their processing and analysis has become necessary with the increaseing size and number of medical images. In particular, computer algorithms for the delineation of anatomical structures and other regions of interest, which are called image segmentation, play a vital role in numerous biomedical imaging applications such as the quantification of tissue volume...

In this paper we present one method for segmenting the lungs and three methods to segment pulmonary lobes from thoracic CT images and their application to the LOLA11 challenge data. The lung segmentation procedure is fully automated and uses a sequence of morphological operations to refine an initial threshold-based segmentation of the pulmonary airspaces. Based on its results, lobe segmentatio...

Knowledge of airway tree morphology has important clinical applications in diagnosis of chronic obstructive pulmonary disease. We present an automatic tree extraction method based on multiple hypothesis tracking and template matching for this purpose and evaluate its performance on chest CT images. The method is adapted from a semi-automatic method devised for vessel segmentation. Idealized tub...

A segmentation framework for the identification of human airway trees in high resolution computed tomography (CT) images is presented. This framework consists of a fully automated segmentation algorithm, supplemented by software editing tools that allow the user to correct the airway segmentation result if needed. The algorithm and tools presented in this paper have been successfully applied on...

RATIONALE AND OBJECTIVES The segmentation of airways from CT images is a critical first step for numerous virtual bronchoscopic (VB) applications. Automatic or semiautomatic methods are necessary, since manual segmentation is prohibitively time consuming. The methods must be robust and operate within a reasonable time frame to be useful for clinical VB use. The authors developed an integrated a...

PURPOSE To present a computer-aided detection tool for identifying, quantifying, and evaluating tuberculosis (TB) cavities in the infected lungs from computed tomography (CT) scans. METHODS The authors' proposed method is based on a novel shape-based automated detection algorithm on CT scans followed by a fuzzy connectedness (FC) delineation procedure. In order to assess interaction between c...

Improvements in the understanding of the physiology of the central airways require an appropriate representation of the non-uniform ventilation at its terminal branches. This paper proposes a new technique for estimating the non-uniform ventilation at the terminal branches by modelling the volume change of their distal peripheral airways, based on vascular segmentation. The vascular tree is use...