An Automatic Segmentation of Bone Tunnels after Anterior Cruciate Ligament Reconstruction in MDCT Image Using K-means Clustering

The anterior cruciate ligament (ACL) reconstruction is usually performed for the injured knee. The ACL reconstruction needs two bone tunnels. It is important to measure changing of the bone tunnel regions after surgery. Thus this study aims to propose an automated segmentation about anteromedial (AM...

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Bibliographic Details
Published in:Procedia computer science 2013, Vol.22, p.590-598
Main Authors: Uozumi, Yosuke, Nagamune, Kouki, Araki, Daisuke, Hoshino, Yuichi, Kuroda, Ryosuke, Kurosaka, Masahiro
Format: Article
Language:English
Subjects:
ACL reconstruction
bone tunnel
MDCT image
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Summary:The anterior cruciate ligament (ACL) reconstruction is usually performed for the injured knee. The ACL reconstruction needs two bone tunnels. It is important to measure changing of the bone tunnel regions after surgery. Thus this study aims to propose an automated segmentation about anteromedial (AM) and posterolateral (PL) parts of the bone tunnels after double bundle ACL reconstruction using k-means clustering. Six patients were evaluated (Age 27 ± 7, four males/two females). As a result, this method could be divided for all patients. This study concluded that the proposed method is enough to divide the bone tunnels of double bundle technique after ACL reconstruction.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2013.09.139