Abstract
In this paper we develop a fully automated method for the segmentation of the femur in axial MR images and its use in the analysis of imaging biomarkers for osteoarthritis (OA). The proposed method is based on anatomical constraints implemented using morphological operations to extract the femur medulla and a level set evolution to extract the femur cortex. The average agreement of the automated segmentation algorithm with ground truth manual segmentations was 0.94 ± 0.03 calculated using the Zijdenbos similarity index (ZSI). A pooled variance t-test analysis found significant associations between the KL grade, a clinical measure of OA severity, and both the cross-sectional area (CSA) of the femur medulla (p = 0.02) and the ratio of the femur medulla CSA to the femur cortex CSA (p = 0.04) for women. No significant association between femur measurements and KL grade was found for men.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
| Subtitle of host publication | Engineering the Future of Biomedicine, EMBC 2009 |
| Publisher | IEEE Computer Society |
| Pages | 6364-6367 |
| Number of pages | 4 |
| ISBN (Print) | 9781424432967 |
| DOIs | |
| State | Published - Jan 1 2009 |
| Externally published | Yes |
| Event | 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 - Minneapolis, MN, United States Duration: Sep 2 2009 → Sep 6 2009 |
Other
| Other | 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 |
|---|---|
| Country | United States |
| City | Minneapolis, MN |
| Period | 9/2/09 → 9/6/09 |
Fingerprint
ASJC Scopus subject areas
- Cell Biology
- Developmental Biology
- Biomedical Engineering
- Medicine(all)
Cite this
An automated method to segment the femur for osteoarthritis research. / Prescott, Jeffrey W.; Pennell, Michael; Best, Thomas; Swanson, Mark S.; Haq, Furqan; Jackson, Rebecca; Gurcan, Metin N.
Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. IEEE Computer Society, 2009. p. 6364-6367 5333257.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An automated method to segment the femur for osteoarthritis research
AU - Prescott, Jeffrey W.
AU - Pennell, Michael
AU - Best, Thomas
AU - Swanson, Mark S.
AU - Haq, Furqan
AU - Jackson, Rebecca
AU - Gurcan, Metin N.
PY - 2009/1/1
Y1 - 2009/1/1
N2 - In this paper we develop a fully automated method for the segmentation of the femur in axial MR images and its use in the analysis of imaging biomarkers for osteoarthritis (OA). The proposed method is based on anatomical constraints implemented using morphological operations to extract the femur medulla and a level set evolution to extract the femur cortex. The average agreement of the automated segmentation algorithm with ground truth manual segmentations was 0.94 ± 0.03 calculated using the Zijdenbos similarity index (ZSI). A pooled variance t-test analysis found significant associations between the KL grade, a clinical measure of OA severity, and both the cross-sectional area (CSA) of the femur medulla (p = 0.02) and the ratio of the femur medulla CSA to the femur cortex CSA (p = 0.04) for women. No significant association between femur measurements and KL grade was found for men.
AB - In this paper we develop a fully automated method for the segmentation of the femur in axial MR images and its use in the analysis of imaging biomarkers for osteoarthritis (OA). The proposed method is based on anatomical constraints implemented using morphological operations to extract the femur medulla and a level set evolution to extract the femur cortex. The average agreement of the automated segmentation algorithm with ground truth manual segmentations was 0.94 ± 0.03 calculated using the Zijdenbos similarity index (ZSI). A pooled variance t-test analysis found significant associations between the KL grade, a clinical measure of OA severity, and both the cross-sectional area (CSA) of the femur medulla (p = 0.02) and the ratio of the femur medulla CSA to the femur cortex CSA (p = 0.04) for women. No significant association between femur measurements and KL grade was found for men.
UR - http://www.scopus.com/inward/record.url?scp=77951001607&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951001607&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2009.5333257
DO - 10.1109/IEMBS.2009.5333257
M3 - Conference contribution
SN - 9781424432967
SP - 6364
EP - 6367
BT - Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - IEEE Computer Society
ER -