Reliable Airway Tree Segmentation Based on Hole Closing in Bronchial Walls

Reliable segmentation of a human airway tree from volumetric computer tomography (CT) data sets is the most important step for further analysis in many clinical applications such as diagnosis of bronchial tree pathologies. In this paper the original airway segmentation algorithm based on discrete topology and geometry is presented. The proposed method is fully automated, reliable and takes advantage of well defined mathematical notions. Holes occur in bronchial walls due to many reasons, for example they are results of noise, image reconstruction artifacts, movement artifacts (heart beat) or partial volume effect (PVE). Holes are common problem in previously proposed methods because in some areas they can cause the segmentation algorithms to leak into surrounding parenchyma parts of a lung. The novelty of the approach consists in the application of a dedicated hole closing algorithm which closes all disturbing holes in a bronchial tree. Having all holes closed the fast region growing algorithm can be applied to make the final segmentation. The proposed method was applied to ten cases of 3D chest CT images. The experimental results showed that the method is reliable, works well in all cases and generate good quality and accurate results.

[1]  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.

[2]  Françoise J. Prêteux,et al.  Advanced navigation tools for virtual bronchoscopy , 2004, IS&T/SPIE Electronic Imaging.

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

[4]  Noboru Niki,et al.  Pulmonary organs analysis for differential diagnosis based on thoracic thin-section CT images , 1997 .

[5]  William E. Higgins,et al.  Robust system for human airway-tree segmentation , 2008, SPIE Medical Imaging.

[6]  Zhengrong Liang,et al.  3D virtual colonoscopy , 1995, Proceedings 1995 Biomedical Visualization.

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

[8]  Gilles Bertrand,et al.  A three-dimensional holes closing algorithm , 1996, Pattern Recognit. Lett..

[9]  Milan Sonka,et al.  Quantitative analysis of pulmonary airway tree structures , 2006, Comput. Biol. Medicine.

[10]  Nicholas Ayache,et al.  Computer Vision, Virtual Reality and Robotics in Medicine , 1995, Lecture Notes in Computer Science.

[11]  Josien P. W. Pluim,et al.  Medical Imaging 2008: Image Processing , 2008 .

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

[13]  Kensaku Mori,et al.  Automated Extraction and Visualization of Bronchus from 3D CT Images of Lung , 1995, CVRMed.

[14]  Charles Lo,et al.  VOLUME VISUALIZATION FOR SURGICAL PLANNING SYSTEM , 2007 .

[15]  김도연,et al.  Virtual Angioscopy for Diagnosis of Carotid Artery Stenosis , 2003 .

[16]  Françoise J. Prêteux,et al.  Quantitative 3D CT bronchography , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

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

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

[19]  Milan Sonka,et al.  Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans , 2005, IEEE Transactions on Medical Imaging.

[20]  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.