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Automatic extraction of three dimensional lung texture tree from HRCT images

机译:从HRCT图像中自动提取三维肺纹理树

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Accurate segmentation of lung texture tree is an essential step for diagnosing pulmonary diseases, including pulmonary emboli and nodules detection, which provides powerful information for research of automatic computer-aided diagnostic (CAD) systems. It still remains a challenging problem because of partial volume effects, high density airway walls and no difference on CT values between arteries and veins. In this paper, we present a novel approach to automatically extract lung tissue textures which contain bronchus and pulmonary veins and arteries. Firstly, we extract the bronchus branch by branch with an adaptive region growing approach. Secondly, a new technique based on selective marking and depth constrained (SMDC)-connection cost is proposed to segment the lung blood vessels. At last, we present a new method to separate the lung blood vessels into pulmonary veins and arteries by using an anatomical feature between each vessel and bronchus. About 91% of arteries and 92% of veins are correctly extracted. The results show that the proposed algorithm provides an automatic and efficient method to extract pulmonary veins and arteries and bronchus.
机译:肺纹理树的准确分割是诊断肺部疾病(包括肺栓塞和结节)的重要步骤,这为研究自动计算机辅助诊断(CAD)系统提供了有力的信息。由于局部容积效应,高密度气道壁以及动脉和静脉之间的CT值没有差异,它仍然是一个具有挑战性的问题。在本文中,我们提出了一种自动提取包含支气管,肺静脉和动脉的肺组织纹理的新颖方法。首先,我们采用自适应区域生长方法逐支提取支气管。其次,提出了一种基于选择性标记和深度受限(SMDC)连接成本的新技术来分割肺血管。最后,我们提出了一种通过使用每个血管和支气管之间的解剖特征将肺血管分为肺静脉和动脉的新方法。正确提取了约91%的动脉和92%的静脉。结果表明,该算法为提取肺静脉,动脉和支气管提供了一种自动有效的方法。

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