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Proceedings Paper

Voxel classification based airway tree segmentation
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Paper Abstract

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.

Paper Details

Date Published: 11 March 2008
PDF: 12 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69141K (11 March 2008); doi: 10.1117/12.772777
Show Author Affiliations
Pechin Lo, Univ. of Copenhagen (Denmark)
Marleen de Bruijne, Univ. of Copenhagen (Denmark)
Univ. Medical Ctr. Rotterdam (Netherlands)

Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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