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A Hybrid Fuzzy Based Algorithm for 3D Human Airway Segmentation

机译:一种用于3D人体气道分割的混合模糊算法

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Segmentation of the human airway tree from volumetric computed tomography images is an important stage for many clinical applications such as virtual bronchoscopy. The main challenges of previously developed methods are to deal with two problems namely, leaking into the surrounding lung parenchyma during segmentation and the need to manually adjust the parameters. To overcome these problems, a multi-seeded fuzzy based region growing approach in conjuction with the spatial information of voxels is proposed. Comparison with a commonly used region growing segmentation algorithm shows that the proposed method retrieves more accurate results by achieving the specificity and sensitivity of 98.81% and 85.18%, respectively. The proposed algorithm needs no manually adjustment of parameters as well as any pre-filtering process, while leading to deliver the clinically accepted segmentation result with no leakage.
机译:从体积计算断层摄影图像中的人气道树的分割是许多临床应用的重要阶段,例如虚拟支气管镜检查。先前开发方法的主要挑战是处理两个问题,即在分割期间泄漏到周围的肺部牙科,并且需要手动调整参数。为了克服这些问题,提出了一种与体素的空间信息配合的多种子模糊基的区域生长方法。与常用区域生长分割算法的比较表明,该方法通过分别实现98.81%和85.18%的特异性和灵敏度来检测更准确的结果。所提出的算法不需要手动调整参数以及任何预过滤过程,同时导致提供临床接受的分段结果,没有泄漏。

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