Airway tree segmentation using adaptive regions of interest

The accurate segmentation of the human airway tree from volumetric CT images builds an important corner stone in pulmonary image processing. It is the basis for many consecutive processing steps like branch-point labeling and matching, virtual bronchoscopy, and more. Previously reported airway tree segmentation methods often suffer from "leaking" into the surrounding lung tissue, caused by the anatomically thin airway wall combined with the occurrence of partial volume effect and noise. Another common problem with previously proposed airway segmentation algorithms is their difficulties with segmenting low dose scans and scans of heavily diseased lungs. We present a new airway tree segmentation method that works in 3D, avoids leaks, and automatically adapts to different types of scans without the need for the user to iteratively adjust any parameters.

[1]  William E. Higgins,et al.  3d image analysis and visualization of tubular structures , 2003 .

[2]  PhengAnn Heng,et al.  Automated extraction of bronchus from 3D CT images of lung based on genetic algorithm and 3D region growing , 2000, Medical Imaging: Image Processing.

[3]  John I. Goutsias,et al.  Mathematical Morphology and its Applications to Image and Signal Processing , 2000, Computational Imaging and Vision.

[4]  Milan Sonka,et al.  Quantitative analysis of three-dimensional tubular tree structures , 2003, SPIE Medical Imaging.

[5]  Milan Sonka,et al.  Branchpoint labeling and matching in human airway trees , 2003, SPIE Medical Imaging.

[6]  Kensaku Mori,et al.  Automated anatomical labeling of the bronchial branch and its application to the virtual bronchoscopy system , 2000, IEEE Transactions on Medical Imaging.

[7]  W E Higgins,et al.  Automatic axis generation for virtual bronchoscopic assessment of major airway obstructions. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[8]  Geoffrey McLennan,et al.  Virtual bronchoscopic assessment of major airway obstructions , 1999, Medical Imaging.

[9]  Milan Sonka,et al.  Segmentation of intrathoracic airway trees: a fuzzy logic approach , 1998, IEEE Transactions on Medical Imaging.

[10]  Milan Sonka,et al.  Knowledge-based segmentation of intrathoracic airways from multidimensional high-resolution CT images , 1994, Medical Imaging.

[11]  Bruno M. Carvalho,et al.  Multiseeded Segmentation Using Fuzzy Connectedness , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Eric A. Hoffman,et al.  Segmentation and quantitation of the primary human airway tree , 1997, Medical Imaging.

[13]  M. Kaneko,et al.  Pulmonary organs analysis for differential diagnosis based on thoracic thin-section CT images , 1997, 1997 IEEE Nuclear Science Symposium Conference Record.

[14]  Cristian Lorenz,et al.  Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy , 2002, SPIE Medical Imaging.

[15]  Javier Portillo,et al.  Breadth-first search and its application to image processing problems , 2001, IEEE Trans. Image Process..

[16]  Françoise J. Prêteux,et al.  Modeling, segmentation, and caliber estimation of bronchi in high resolution computerized tomography , 1999, J. Electronic Imaging.