Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee

Fripp, Jurgen, Crozier, Stuart, Warfield, Simon K. and Ourselin, Sébastien (2010) Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Transactions on Medical Imaging, 29 1: 55-64. doi:10.1109/TMI.2009.2024743


Author Fripp, Jurgen
Crozier, Stuart
Warfield, Simon K.
Ourselin, Sébastien
Title Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee
Journal name IEEE Transactions on Medical Imaging   Check publisher's open access policy
ISSN 0278-0062
1558-254X
Publication date 2010-01-01
Year available 2009
Sub-type Article (original research)
DOI 10.1109/TMI.2009.2024743
Volume 29
Issue 1
Start page 55
End page 64
Total pages 10
Place of publication Piscataway, NJ, United States
Publisher IEEE
Collection year 2011
Language eng
Subject 1004 Medical Biotechnology
Formatted abstract
In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm.
© Copyright 2011 IEEE – All Rights Reserved

Keyword Bone
Cartilage
Deformable models
Magnetic resonance imaging (MRI)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes First Published: 10 June 2009

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Sun, 24 Jan 2010, 10:04:23 EST