Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration
Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration
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DOI：
10.1109/tmi.2013.2290491
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发表时间：
2014-02-01
影响因子：
10.6
通讯作者：
McDonald, Clement J.
中科院分区：
文献类型：
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作者：
Candemir, Sema;Jaeger, Stefan;McDonald, Clement J.
美国国家医学图书馆（NLM）正在开发一种数字化胸部X射线（CXR）筛查系统，用于全球资源有限的社区和发展中国家，重点是结核病的早期发现。数字CXR的计算机辅助诊断中的一个关键组成部分是肺部区域的自动检测。在本文中，我们提出了一种非刚性配准驱动的鲁棒肺分割方法，使用基于图像检索的患者特定自适应肺模型，检测肺边界，超越最先进的性能。该方法包括三个主要阶段：1）基于内容的图像检索方法，用于识别训练图像2）使用SIFT流创建肺形状的初始患者特异性解剖模型，用于训练掩模到患者CXR的可变形配准，以及3）使用具有定制能量函数的图切割优化方法来提取细化的肺边界。我们在公共JSRT数据库上的平均准确率为95.4%，是已发表结果中最高的。分别来自美国马里兰州蒙哥马利县和印度的两个新CXR数据集的相似准确度分别为94.1%和91.7%，证明了我们的肺部分割方法的鲁棒性。
The National Library of Medicine (NLM) is developing a digital chest X-ray (CXR) screening system for deployment in resource constrained communities and developing countries worldwide with a focus on early detection of tuberculosis. A critical component in the computer-aided diagnosis of digital CXRs is the automatic detection of the lung regions. In this paper, we present a nonrigid registration-driven robust lung segmentation method using image retrieval-based patient specific adaptive lung models that detects lung boundaries, surpassing state-of-the-art performance. The method consists of three main stages: 1) a content-based image retrieval approach for identifying training images (with masks) most similar to the patient CXR using a partial Radon transform and Bhattacharyya shape similarity measure, 2) creating the initial patient-specific anatomical model of lung shape using SIFT-flow for deformable registration of training masks to the patient CXR, and 3) extracting refined lung boundaries using a graph cuts optimization approach with a customized energy function. Our average accuracy of 95.4% on the public JSRT database is the highest among published results. A similar degree of accuracy of 94.1% and 91.7% on two new CXR datasets from Montgomery County, MD, USA, and India, respectively, demonstrates the robustness of our lung segmentation approach.