Airway Labeling Using A Hidden Markov Tree Model.

Abstract : We present a novel airway labeling algorithm based on a Hidden Markov Tree Model (HMTM). We obtain a collection of discrete points along the segmented airway tree using particles sampling [1] and establish topology using Kruskal's minimum spanning tree algorithm. Following this, our HMTM algorithm probabilistically assigns labels to each point. While alternative methods label airway branches out to the segmental level, we describe a general method and demonstrate its performance out to the subsubsegmental level (two generations further than previously published approaches). We present results on a collection of 25 computed tomography (CT) datasets taken from a Chronic Obstructive Pulmonary Disease (COPD) study.
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Communication dans un congrès
International Symposium in Biomedical Imaging, Apr 2014, Beijing, China. 2014, pp.554-558, 2014
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https://hal.inria.fr/hal-01153117
Contributeur : Demian Wassermann <>
Soumis le : mardi 19 mai 2015 - 10:22:40
Dernière modification le : vendredi 8 décembre 2017 - 14:26:13

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James C Ross, Alejandro A Díaz, Yuka Okajima, Demian Wassermann, George R Washko, et al.. Airway Labeling Using A Hidden Markov Tree Model.. International Symposium in Biomedical Imaging, Apr 2014, Beijing, China. 2014, pp.554-558, 2014. 〈hal-01153117〉

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