Automatic analysis of trabecular bone structure from knee MRI

Joselene Marques; Rabia Granlund; Martin Lillholm; Paola C. Pettersen; Erik B.  Dam

doi:10.1016/j.compbiomed.2012.04.005

Automatic analysis of trabecular bone structure from knee MRI

Joselene Marques, Rabia Granlund, Martin Lillholm, Paola C. Pettersen, Erik B. Dam

Datalogisk Institut

4 Citationer (Scopus)

Abstract

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

Originalsprog	Engelsk
Tidsskrift	Computers in Biology and Medicine
Vol/bind	42
Udgave nummer	7
Sider (fra-til)	735-742
Antal sider	8
ISSN	0010-4825
DOI	https://doi.org/10.1016/j.compbiomed.2012.04.005
Status	Udgivet - jul. 2012

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10.1016/j.compbiomed.2012.04.005

Automatic analysis of trabecular bone structure from knee MRIForlagets udgivne version, 352 KB

Citationsformater

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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.}",

year = "2012",

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doi = "10.1016/j.compbiomed.2012.04.005",

language = "English",

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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.

PY - 2012/7

Y1 - 2012/7

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

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SP - 735

EP - 742

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

IS - 7

ER -

Automatic analysis of trabecular bone structure from knee MRI

Abstract

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