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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >Voxel classification based airway tree segmentation
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Voxel classification based airway tree segmentation

机译：基于体素分类的气道树分割

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摘要

This paper presents a voxel classification based method for segmenting the human airway tree in volumetric computed tomography (CT) images. In contrast to standard methods that use only voxel intensities, our method uses a more complex appearance model based on a set of local image appearance features and Kth nearest neighbor (KNN) classification. The optimal set of features for classification is selected automatically from a large set of features describing the local image structure at several scales. The use of multiple features enables the appearance model to differentiate between airway tree voxels and other voxels of similar intensities in the lung, thus making the segmentation robust to pathologies such as emphysema. The classifier is trained on imperfect segmentations that can easily be obtained using region growing with a manual threshold selection. Experiments show that the proposed method results in a more robust segmentation that can grow into the smaller airway branches without leaking into emphysematous areas, and is able to segment many branches that are not present in the training set.
机译：本文提出了一种基于体素分类的方法，用于在体积计算机断层扫描（CT）图像中分割人的气道树。与仅使用体素强度的标准方法相比，我们的方法基于一组局部图像外观特征和Kth最近邻（KNN）分类，使用了更复杂的外观模型。从描述几个比例的局部图像结构的大量特征中自动选择最佳的分类特征集。多种功能的使用使外观模型能够区分气道树体素和肺中具有类似强度的其他体素，从而使分割对诸如肺气肿等病理学具有鲁棒性。对分类器进行不完美分割的训练，可以使用手动阈值选择的区域增长轻松获得不完美分割。实验表明，所提出的方法可以实现更健壮的分割，可以成长为较小的气道分支，而不会泄漏到气肿区域，并且可以分割训练集中不存在的许多分支。

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