3D automatic lung segmentation in low-dose CT

F. Nery, J. S. Silva, N. Ferreira, F. Caramelo
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引用次数: 5

Abstract

The amount of information generated by medical imaging procedures as well as the number of exams performed all over the world is increasing over time. This leads to the need of faster and more efficient ways to deal with the large datasets characteristic of these procedures. Computer-aided diagnostic methods have an important role in this area. This paper presents a fully automatic method for the identification of the lungs in CT images. The lung regions are identified by a threshold operation as a first step. To separate merged lungs, we apply a sequence of morphological operations. Additionally the trachea and large airways are identified and removed in each slice. The proposed approach was tested in several whole-body CT studies presenting positive results.
低剂量CT三维肺自动分割
随着时间的推移,医学成像程序产生的信息量以及在世界各地进行的检查数量都在增加。这导致需要更快和更有效的方法来处理这些程序的大数据集特征。计算机辅助诊断方法在这一领域发挥着重要作用。本文提出了一种用于CT图像中肺的全自动识别方法。作为第一步,通过阈值操作识别肺区域。为了分离合并的肺,我们采用一系列形态学手术。此外,在每个切片上识别并切除气管和大气道。该方法在几项全身CT研究中进行了测试,显示出积极的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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