English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

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.

Voxel classification based airway tree segmentation