Extraction of airway trees using multiple hypothesis tracking and template matching
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Extraction of airway trees using multiple hypothesis tracking and template matching. / Raghavendra, Selvan; Petersen, Jens; Pedersen, Jesper Johannes Holst; de Bruijne, Marleen.
The Sixth International Workshop on Pulmonary Image Analysis: Athens, Greece - October 21, 2016. red. / Reinhard R. Beichel; Keyvan Farahani; Colin Jacobs; Sven Kabus; Atilla P. Kiraly; Jan-Martin Kuhnigk; Jamie R. McClelland; Kensaku Mori; Jens Petersen; Simon Rit. Create Space Independent Publishing Platform, 2016. s. 43-54.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Extraction of airway trees using multiple hypothesis tracking and template matching
AU - Raghavendra, Selvan
AU - Petersen, Jens
AU - Pedersen, Jesper Johannes Holst
AU - de Bruijne, Marleen
N1 - Conference code: 6
PY - 2016
Y1 - 2016
N2 - Knowledge of airway tree morphology has important clinical applications in diagnosis of chronic obstructive pulmonary disease. We present an automatic tree extraction method based on multiple hypothesis tracking and template matching for this purpose and evaluate its performance on chest CT images. The method is adapted from a semi-automatic method devised for vessel segmentation. Idealized tubular templates are constructed that match airway probability obtained from a trained classifier and ranked based on their relative significance. Several such regularly spaced templates form the local hypotheses used in constructing a multiple hypothesis tree, which is then traversed to reach decisions. The proposed modifications remove the need for local thresholding of hypotheses as decisions are made entirely based on statistical comparisons involving the hypothesis tree. The results show improvements in performance when compared to the original method and region growing on intensity images. We also compare the method with region growing on the probability images, where the presented method does not show substantial improvement, but we expect it to be less sensitive to local anomalies in the data.
AB - Knowledge of airway tree morphology has important clinical applications in diagnosis of chronic obstructive pulmonary disease. We present an automatic tree extraction method based on multiple hypothesis tracking and template matching for this purpose and evaluate its performance on chest CT images. The method is adapted from a semi-automatic method devised for vessel segmentation. Idealized tubular templates are constructed that match airway probability obtained from a trained classifier and ranked based on their relative significance. Several such regularly spaced templates form the local hypotheses used in constructing a multiple hypothesis tree, which is then traversed to reach decisions. The proposed modifications remove the need for local thresholding of hypotheses as decisions are made entirely based on statistical comparisons involving the hypothesis tree. The results show improvements in performance when compared to the original method and region growing on intensity images. We also compare the method with region growing on the probability images, where the presented method does not show substantial improvement, but we expect it to be less sensitive to local anomalies in the data.
M3 - Article in proceedings
SN - 978-1-5370-3858-2
SP - 43
EP - 54
BT - The Sixth International Workshop on Pulmonary Image Analysis
A2 - Beichel, Reinhard R.
A2 - Farahani, Keyvan
A2 - Jacobs, Colin
A2 - Kabus, Sven
A2 - Kiraly, Atilla P.
A2 - Kuhnigk, Jan-Martin
A2 - McClelland, Jamie R.
A2 - Mori, Kensaku
A2 - Petersen, Jens
A2 - Rit, Simon
PB - Create Space Independent Publishing Platform
Y2 - 21 October 2016 through 21 October 2016
ER -
ID: 172023825