北京生物醫(yī)學(xué)工程

最優(yōu)閾值生長和形態(tài)學(xué)結(jié)合的肺氣道樹分割方法

Pulmonary Airway Tree Segmentation by Combining Optimal Threshold Region Grow and Morphology

作者：王昌黃煜峰王興家馮煥清李傳富

單位：中國科學(xué)技術(shù)大學(xué)電子科學(xué)與技術(shù)系(合肥230027)

關(guān)鍵詞：灰度尺度重建；肺部氣道樹分割；最優(yōu)閾值區(qū)域生長；高分辨率CT；形態(tài)學(xué)算子

分類號(hào)：

出版年·卷·期（頁碼）：2010·29·3（241-244）

摘要：

在肺氣道樹分割的過程中,由于部分容積效應(yīng)和噪聲污染的影響,容易出現(xiàn)支氣管斷裂和分割泄漏現(xiàn)象，因此不能分割出精確肺部氣道樹。為此本文提出一種最優(yōu)閾值生長和形態(tài)學(xué)結(jié)合的氣道樹分割方法。首先利用最優(yōu)閾值生長算法分割初略的肺部氣道樹，利用灰度重建的形態(tài)學(xué)算子提取潛在的精細(xì)肺氣管區(qū)域，然后將上述兩種分割結(jié)果合成一個(gè)完整的肺部氣道樹，最后利用種子點(diǎn)區(qū)域生長法去除結(jié)果中的偽氣管區(qū)域，得到包含第5級(jí)以及約60%第6級(jí)的支氣管。本方法有效解決了高精度肺氣道樹分割中的支氣管斷裂和泄漏問題，有較好的魯棒性。

In the process of pulmonary airway tree segmentation, it is prone to bronchial rupture and segmentation leakage, during to partial volume effects and noise pollution. In this paper, we proposed the hybrid method which was composed of optimal threshold region grow and morphology for pulmonary airway tree segmentation. Firstly the optimal threshold region growth algorithm was used to obtain low-level of airway tree, and the grayscale reconstruction morphological operator was used to extract fine potential regions of airway. Then by combining these two results of segmentation method, we obtained the integrated pulmonary airway tree. Lastly the method of region grow was used on integrated data set to remove pseudo-tracheal regions to ensure the three-dimensional connectivity of airway tree, including the trachea at level 5 and about 60% of the trachea at level 6. The proposed method effectively solved the problems of bronchial rupture and segmentation leakage, in the segmentation of high-precision airway tree, and had a good robustness.

參考文獻(xiàn)：

［1］Juerg Tschirrena, Hoffman EA. Airway tree segmentation using adaptive regions of interest Proceedings of SPIE, 2004, 5369:117-124.
［2］Atilla PK, William E, Higgins GM. Three-dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy. Academic Radiology, 2002, 9(10):1153-1167.
［3］趙鵬, 馮煥清, 李傳富,等. 一種基于參數(shù)自適應(yīng)的肺氣道樹分割方法. 北京生物醫(yī)學(xué)工程,2009,28(5):454-457.
［4］Schlathter T,Lorenz C,Carlsen S,et al. Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy. SPIE Medical Imaging, 2002, 4684:103-113.
［5］Cohen LD, Deschamps T. Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging. Computer Methods in Biomechanics and Biomedical Engineering, 2007. DOI: 10.1080/10255840701328239.
［6］Chandrasekhar P,Zerhcuni E, Mitzner W.Segmentation of 3d pulmonary trees using mathematical morphology. CHANDRASEKHAR PISUPATI ET AL http://citeseerx.ist.psu.edu/view.
［7］Fetita, Preteux CI. Pulmonary Airways: 3-D Reconstruction From Multislice CT and Clinical Investigation. IEEE Trans on Medical Imaging, 2004, 23(11):1353-1363
［8］Preteux F. On a distance function approach for gray-level mathematical morphology. //Edward Dougherty, ed.Mathematical morphology in image processing.New York: M. Dekker, 1993.
［9］Francoise P, Capderou A, Grenier P. Modeling, segmentation, and caliber estimation of bronchi in high resolution computerized tomography. Journal of Electronic Imaging, 1999, 8(1):35-46.

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