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首页> 外文期刊>Journal of digital imaging: the official journal of the Society for Computer Applications in Radiology >Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases
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Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

机译：HRCT扫描对弥漫性实质性肺疾病的自动肺分割

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摘要

Performing accurate and fully automated lung segmentation of high-resolution computed tomography (HRCT) images affected by dense abnormalities is a challenging problem. This paper presents a novel algorithm for automated segmentation of lungs based on modified convex hull algorithm and mathematical morphology techniques. Sixty randomly selected lung HRCT scans with different abnormalities are used to test the proposed algorithm, and experimental results show that the proposed approach can accurately segment the lungs even in the presence of disease patterns, with some limitations in the apices and bases of lungs. The algorithm demonstrates a high segmentation accuracy (dice similarity coefficient = 98.62 and shape differentiation metrics d(mean) = 1.39 mm, and d(rms) = 2.76 mm). Therefore, the developed automated lung segmentation algorithm is a good candidate for the first stage of a computer-aided diagnosis system for diffuse lung diseases.
机译：对受密集异常影响的高分辨率计算机断层扫描（HRCT）图像执行准确，全自动的肺部分割是一个难题。本文提出了一种基于改进的凸包算法和数学形态学技术的肺自动分割算法。使用六十个随机选择的具有不同异常的肺HRCT扫描来测试所提出的算法，实验结果表明，即使在存在疾病模式的情况下，所提出的方法也可以准确地对肺进行分割，但在肺的顶点和根基方面存在一些限制。该算法证明了很高的​​分割精度（骰子相似系数= 98.62，形状区分指标d（mean）= 1.39 mm，d（rms）= 2.76 mm）。因此，对于弥漫性肺部疾病的计算机辅助诊断系统的第一阶段，开发的自动肺部分割算法是不错的选择。

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