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Methods Inf Med. 2012;51(5):406-14. doi: 10.3414/ME11-02-0033. Epub 2012 Sep 28.

Automatic joint alignment measurements in pre- and post-operative long leg standing radiographs.

Author information

  • 1André Gooßen, Philips Research, Philips Technologie GmbH - Innovative Technologies, 22335 Hamburg, Germany. andre.goossen@philips.com

Abstract

OBJECTIVES:

For diagnosis or treatment assessment of knee joint osteoarthritis it is required to measure bone morphometry from radiographic images. We propose a method for automatic measurement of joint alignment from pre-operative as well as post-operative radiographs.

METHODS:

In a two step approach we first detect and segment any implants or other artificial objects within the image. We exploit physical characteristics and avoid prior shape information to cope with the vast amount of implant types. Subsequently, we exploit the implant delineations to adapt the initialization and adaptation phase of a dedicated bone segmentation scheme using deformable template models. Implant and bone contours are fused to derive the final joint segmentation and thus the alignment measurements.

RESULTS:

We evaluated our method on clinical long leg radiographs and compared both the initialization rate, corresponding to the number of images successfully processed by the proposed algorithm, and the accuracy of the alignment measurement. Ground truth has been generated by an experienced orthopedic surgeon. For comparison a second reader reevaluated the measurements. Experiments on two sets of 70 and 120 digital radiographs show that 92% of the joints could be processed automatically and the derived measurements of the automatic method are comparable to a human reader for pre-operative as well as post-operative images with a typical error of 0.7° and correlations of r = 0.82 to r = 0.99 with the ground truth.

CONCLUSIONS:

The proposed method allows deriving objective measures of joint alignment from clinical radiographs. Its accuracy and precision are on par with a human reader for all evaluated measurements.

PMID:
23038612
[PubMed - indexed for MEDLINE]
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