Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy.

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 airway segmentation system and demonstrated its effectiveness on a series of human images. MATERIALS AND METHODS The authors' airway segmentation system draws on two segmentation algorithms: (a) an adaptive region-growing algorithm and (b) a new hybrid algorithm that uses both region growing and mathematical morphology. Images from an ongoing VB study were segmented by means of both the adaptive region-growing and the new hybrid methods. The segmentation volume, branch number estimate, and segmentation quality were determined for each case. RESULTS The results demonstrate the need for an integrated segmentation system, since no single method is superior for all clinically relevant cases. The region-growing algorithm is the fastest and provides acceptable segmentations for most VB applications, but the hybrid method provides superior airway edge localization, making it better suited for quantitative applications. In addition, the authors show that prefiltering the image data before airway segmentation increases the robustness of both region-growing and hybrid methods. CONCLUSION The combination of these two algorithms with the prefiltering options allowed the successful segmentation of all test images. The times required for all segmentations were acceptable, and the results were suitable for the authors' VB application needs.

[1]  Walter J. Karplus,et al.  Virtual reality in radiology: virtual intervention , 1995, Medical Imaging.

[2]  Lawrence B. Wolff,et al.  Tracking 3-D pulmonary tree structures , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[3]  Pierre-Martin Tardif,et al.  Unsupervised partial volume estimation using 3D and statistical priors , 2001, SPIE Medical Imaging.

[4]  K Ramaswamy,et al.  Virtual bronchoscopy for three--dimensional pulmonary image assessment: state of the art and future needs. , 1998, Radiographics : a review publication of the Radiological Society of North America, Inc.

[5]  D R Haynor,et al.  Partial volume tissue classification of multichannel magnetic resonance images-a mixel model. , 1991, IEEE transactions on medical imaging.

[6]  E. Hoffman,et al.  Assessment of the pulmonary structure-function relationship and clinical outcomes measures: quantitative volumetric CT of the lung. , 1997, Academic radiology.

[7]  E A Hoffman,et al.  Measurement of three-dimensional lung tree structures by using computed tomography. , 1995, Journal of applied physiology.

[8]  Milan Sonka,et al.  Validation of an enhanced knowledge-based method for segmentation and quantitative analysis of intrathoracic airway trees from three-dimensional CT images , 1995, Medical Imaging.

[9]  Y Itzchak,et al.  Virtual bronchoscopy in children: early clinical experience. , 1998, AJR. American journal of roentgenology.

[10]  Geoffrey McLennan,et al.  3D human airway segmentation for virtual bronchoscopy , 2002, SPIE Medical Imaging.

[11]  E A Hoffman,et al.  Assessment of methacholine-induced airway constriction by ultrafast high-resolution computed tomography. , 1993, Journal of applied physiology.

[12]  Milan Sonka,et al.  Rule-based detection of intrathoracic airway trees , 1996, IEEE Trans. Medical Imaging.

[13]  Geoffrey McLennan,et al.  Virtual bronchoscopic approach for combining 3D CT and endoscopic video , 2000, Medical Imaging.

[14]  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.

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

[16]  Kensaku Mori,et al.  Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[17]  Willi A. Kalender,et al.  Computed tomography : fundamentals, system technology, image quality, applications , 2000 .

[18]  Francoise J. Preteux,et al.  Bronchial tree modeling and 3D reconstruction , 2000, SPIE Optics + Photonics.

[19]  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.

[20]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[21]  Karl-Hans Englmeier,et al.  Virtual bronchoscopy based on spiral CT images , 1998, Medical Imaging.

[22]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[23]  Eric A. Hoffman,et al.  Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images , 2001, IEEE Transactions on Medical Imaging.

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

[25]  H. Soltanian-Zadeh,et al.  Optimal transformation for correcting partial volume averaging effects in magnetic resonance imaging , 1992, IEEE Conference on Nuclear Science Symposium and Medical Imaging.

[26]  Geoffrey McLennan,et al.  Experiments in virtual-endoscopic guidance of bronchoscopy , 2001, SPIE Medical Imaging.

[27]  Eric A. Hoffman,et al.  Accurate measurement of intrathoracic airways , 1997, IEEE Transactions on Medical Imaging.

[28]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  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.

[30]  H. Donald Gage,et al.  Statistical models of partial volume effect , 1995, IEEE Trans. Image Process..

[31]  J. A. Campbell ACADEMIC RADIOLOGY. , 1965, The American journal of roentgenology, radium therapy, and nuclear medicine.

[32]  R M Summers,et al.  Virtual bronchoscopy: segmentation method for real-time display. , 1996, Radiology.

[33]  V. Sauret,et al.  Semi-automated tabulation of the 3D topology and morphology of branching networks using CT: application to the airway tree. , 1999, Physics in medicine and biology.

[34]  D J Vining,et al.  Virtual bronchoscopy. , 1999, Clinics in chest medicine.

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

[36]  V Argiro,et al.  Perspective volume rendering of CT and MR images: applications for endoscopic imaging. , 1996, Radiology.

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

[38]  Lawrence B. Wolff,et al.  Segmentation of 3D Pulmonary Trees Using Mathematical Morphology , 1996, ISMM.

[39]  Janice Z. Turlington,et al.  New techniques for efficient sliding thin-slab volume visualization , 2001, IEEE Transactions on Medical Imaging.

[40]  William Schroeder,et al.  The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics , 1997 .

[41]  R. Spillane,et al.  High-resolution CT of the lungs. , 1993, American family physician.

[42]  Geoffrey McLennan,et al.  Automatic axis generation for 3D virtual-bronchoscopic image assessment , 1998, Medical Imaging.

[43]  D. Aykac,et al.  Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images , 2003, IEEE Transactions on Medical Imaging.

[44]  William E. Higgins,et al.  Technique for registering 3D virtual CT images to endoscopic video , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).