Segmentation, Skeletonization, and Branchpoint Matching — A Fully Automated Quantitative Evaluation of Human Intrathoracic Airway Trees

J Tschirren; K Palágyi; J. M Reinhardt; E. A Hoffman; M Sonka

doi:10.1007/3-540-45787-9_2

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Segmentation, Skeletonization, and Branchpoint Matching — A Fully Automated Quantitative Evaluation of Human Intrathoracic Airway Trees

Book chapter

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Peer reviewed

Segmentation, Skeletonization, and Branchpoint Matching — A Fully Automated Quantitative Evaluation of Human Intrathoracic Airway Trees

J Tschirren, K Palágyi, J. M Reinhardt, E. A Hoffman and M Sonka

Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002, pp.12-19

Lecture Notes in Computer Science, Springer Berlin Heidelberg

10/10/2002

DOI: 10.1007/3-540-45787-9_2

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Abstract

Modern multislice X-ray CT scanners provide high-resolution volumetric image data containing a wealth of structural and functional information. The size of the volumes makes it more and more difficult for human observers to visually evaluate their contents. Similar to other areas of medical image analysis, highly automated extraction and quantitative assessment of volumetric data is increasingly important in pulmonary physiology, diagnosis, and treatment. We present a method for a fully automated segmentation of a human airway tree, its skeletonization, identification of airway branches and branchpoints, as well as a method for matching the airway trees, branches, and branchpoints for the same subject over time and across subjects. The validation of our method shows a high correlation between the automatically obtained results and reference data provided by human observers.

Airway Tree

Functional Residual Capacity

Human Observer

Maximum Clique

Total Lung Capacity

Details

Title: Subtitle: Segmentation, Skeletonization, and Branchpoint Matching — A Fully Automated Quantitative Evaluation of Human Intrathoracic Airway Trees
Creators: J Tschirren - University of Iowa
K Palágyi - University of Szeged
J. M Reinhardt - University of Iowa
E. A Hoffman - University of Iowa
M Sonka - University of Iowa
Resource Type: Book chapter
Publication Details: Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002, pp.12-19
Series: Lecture Notes in Computer Science
DOI: 10.1007/3-540-45787-9_2
eISSN: 1611-3349
ISSN: 0302-9743
Publisher: Springer Berlin Heidelberg; Berlin, Heidelberg
Language: English
Date published: 10/10/2002
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Internal Medicine; Ophthalmology and Visual Sciences
Record Identifier: 9984186693002771

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