Line Detection as an Inverse Problem: Application to Lung Ultrasound Imaging.
Line Detection as an Inverse Problem: Application to Lung Ultrasound Imaging.
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DOI：
10.1109/tmi.2017.2715880
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发表时间：
2017-10
影响因子：
10.6
通讯作者：
Achim A
中科院分区：
文献类型：
--
作者：
Anantrasirichai N;Hayes W;Allinovi M;Bull D;Achim A
提出了一种新的散斑图像线恢复方法。我们使用基于Radon变换和稀疏性正则化的凸和非凸优化技术来解决这个稀疏估计问题。这分解为子问题，这些子问题使用乘法器的交替方向方法来解决，从而同时实现线检测和反卷积。我们通过全变差盲反卷积在Radon域中包含一个额外的去模糊步骤，以增强线可视化并改善线识别。我们评估我们的方法在一个真正的临床应用：识别肺超声图像中的b线。因此，提出了一种自动b线识别方法，使用Radon变换域的简单局部最大值技术，并与已知的临床线伪影定义相关联。使用所有最初检测到的线作为起点，我们的方法然后区分b线和其他没有临床意义的线，包括z线和a线。我们评估了我们的技术使用作为地面真实线由临床专家视觉识别。当对b线检测和反卷积同时采用非凸正则化时，所提出的方法通过F分数来测量b线检测的最佳性能。F评分和受试者工作特征（ROC）曲线显示，该方法优于目前最先进的方法，b线检测性能分别提高了54%、40%和33%，根据ROC曲线评估，b线检测性能提高了24%。
This paper presents a novel method for line restoration in speckle images. We address this as a sparse estimation problem using both convex and non-convex optimization techniques based on the Radon transform and sparsity regularization. This breaks into subproblems, which are solved using the alternating direction method of multipliers, thereby achieving line detection and deconvolution simultaneously. We include an additional deblurring step in the Radon domain via a total variation blind deconvolution to enhance line visualization and to improve line recognition. We evaluate our approach on a real clinical application: the identification of B-lines in lung ultrasound images. Thus, an automatic B-line identification method is proposed, using a simple local maxima technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Using all initially detected lines as a starting point, our approach then differentiates between B-lines and other lines of no clinical significance, including Z-lines and A-lines. We evaluated our techniques using as ground truth lines identified visually by clinical experts. The proposed approach achieves the best B-line detection performance as measured by the F score when a non-convex regularization is employed for both line detection and deconvolution. The F scores as well as the receiver operating characteristic (ROC) curves show that the proposed approach outperforms the state-of-the-art methods with improvements in B-line detection performance of 54%, 40%, and 33% for , , and , respectively, and of 24% based on ROC curve evaluations.