A4 Refereed article in a conference publication
Analysis of Trabecular Bone Microstructure Using Contour Tree Connectivity
Authors: Aydogan DB, Moritz N, Aro HT, Hyttinen J
Editors: Mori K., Sakuma I., Sato Y., Barillot C., Navab N.
Conference name: International Conference on Medical Image Computing and Computer-Assisted Intervention
Publisher: SPRINGER-VERLAG BERLIN
Publishing place: Nagoya, Japan
Publication year: 2013
Journal: Lecture Notes in Computer Science
Book title : Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Series title: Lecture Notes in Computer Science
Volume: 8150
First page : 428
Last page: 435
ISBN: 978-3-642-40762-8
eISBN: 978-3-642-40763-5
ISSN: 0302-9743
DOI: https://doi.org/10.1007/978-3-642-40763-5_53
Abstract
Millions of people worldwide suffer from fragility fractures, which cause significant morbidity, financial costs and even mortality. The gold standard to quantify structural properties of trabecular bone is based on the morphometric parameters obtained from mu CT images of clinical bone biopsy specimens. The currently used image processing approaches are not able to fully explain the variation in bone strength. In this study, we introduce the contour tree connectivity (CTC) as a novel morphometric parameter to study trabecular bone quality. With CTC, we calculate a new connectivity measure for trabecular bone by using contour tree representation of binary images and algebraic graph theory. To test our approach, we use trabecular bone biopsies obtained from 55 female patients. We study the correlation of CTC with biomechanical test results as well as other morphometric parameters obtained from mu CT. The results based on our dataset show that CTC is the 3rd best predictive feature of ultimate bone strength after bone volume fraction and degree of anisotropy.
Millions of people worldwide suffer from fragility fractures, which cause significant morbidity, financial costs and even mortality. The gold standard to quantify structural properties of trabecular bone is based on the morphometric parameters obtained from mu CT images of clinical bone biopsy specimens. The currently used image processing approaches are not able to fully explain the variation in bone strength. In this study, we introduce the contour tree connectivity (CTC) as a novel morphometric parameter to study trabecular bone quality. With CTC, we calculate a new connectivity measure for trabecular bone by using contour tree representation of binary images and algebraic graph theory. To test our approach, we use trabecular bone biopsies obtained from 55 female patients. We study the correlation of CTC with biomechanical test results as well as other morphometric parameters obtained from mu CT. The results based on our dataset show that CTC is the 3rd best predictive feature of ultimate bone strength after bone volume fraction and degree of anisotropy.
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