Automatic analysis of trabecular bone structure from knee MRI

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Standard

Automatic analysis of trabecular bone structure from knee MRI. / Marques, Joselene; Granlund, Rabia; Lillholm, Martin; Pettersen, Paola C.; Dam, Erik B. .

I: Computers in Biology and Medicine, Bind 42, Nr. 7, 2012, s. 735-742.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Marques, J, Granlund, R, Lillholm, M, Pettersen, PC & Dam, EB 2012, 'Automatic analysis of trabecular bone structure from knee MRI', Computers in Biology and Medicine, bind 42, nr. 7, s. 735-742. https://doi.org/10.1016/j.compbiomed.2012.04.005

APA

Marques, J., Granlund, R., Lillholm, M., Pettersen, P. C., & Dam, E. B. (2012). Automatic analysis of trabecular bone structure from knee MRI. Computers in Biology and Medicine, 42(7), 735-742. https://doi.org/10.1016/j.compbiomed.2012.04.005

Vancouver

Marques J, Granlund R, Lillholm M, Pettersen PC, Dam EB. Automatic analysis of trabecular bone structure from knee MRI. Computers in Biology and Medicine. 2012;42(7):735-742. https://doi.org/10.1016/j.compbiomed.2012.04.005

Author

Marques, Joselene ; Granlund, Rabia ; Lillholm, Martin ; Pettersen, Paola C. ; Dam, Erik B. . / Automatic analysis of trabecular bone structure from knee MRI. I: Computers in Biology and Medicine. 2012 ; Bind 42, Nr. 7. s. 735-742.

Bibtex

@article{e6608ae37bb4424abd28614233d3d4c8,
title = "Automatic analysis of trabecular bone structure from knee MRI",
abstract = "We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker. The performance of the developed marker reached a generalization area-under-the-ROC (AUC) of 0.82, which is higher than the established cartilage markers known to relate to the OA diagnosis.",
author = "Joselene Marques and Rabia Granlund and Martin Lillholm and Pettersen, {Paola C.} and Dam, {Erik B.}",
note = "Copyright {\circledC} 2012 Elsevier Ltd. All rights reserved.",
year = "2012",
doi = "10.1016/j.compbiomed.2012.04.005",
language = "English",
volume = "42",
pages = "735--742",
journal = "Computers in Biology and Medicine",
issn = "0010-4825",
publisher = "Pergamon Press",
number = "7",

}

RIS

TY - JOUR

T1 - Automatic analysis of trabecular bone structure from knee MRI

AU - Marques, Joselene

AU - Granlund, Rabia

AU - Lillholm, Martin

AU - Pettersen, Paola C.

AU - Dam, Erik B.

N1 - Copyright © 2012 Elsevier Ltd. All rights reserved.

PY - 2012

Y1 - 2012

N2 - We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker. The performance of the developed marker reached a generalization area-under-the-ROC (AUC) of 0.82, which is higher than the established cartilage markers known to relate to the OA diagnosis.

AB - We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker. The performance of the developed marker reached a generalization area-under-the-ROC (AUC) of 0.82, which is higher than the established cartilage markers known to relate to the OA diagnosis.

U2 - 10.1016/j.compbiomed.2012.04.005

DO - 10.1016/j.compbiomed.2012.04.005

M3 - Journal article

C2 - 22579046

VL - 42

SP - 735

EP - 742

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

IS - 7

ER -

ID: 40995413