Fully automatic joint segmentation for computer-aided diagnosis and planning

Authors:
André Gooßen;Thomas Pralow;Rolf-Rainer Grigat
Affiliations:
Vision Systems, Hamburg University of Technology, Hamburg, Germany and Diagnostic X-Ray, Philips Healthcare, Hamburg, Germany;Diagnostic X-Ray, Philips Healthcare, Hamburg, Germany;Vision Systems, Hamburg University of Technology, Hamburg, Germany
Venue:
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Year:
2010

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Abstract

Orthopaedic examinations are a major reason for radiographic image acquisition. For many diagnostic problems measurements have to be computed from the recorded radiographs. As this task is time-consuming and lacks objectivity, it is desirable to perform these measurements automatically via so-called computational imaging. This requires robust and accurate methods trained and proven on clinical data. We propose a fully automatic technique and present the three complementing stages of our segmentation algorithm. We evaluated the proposed method on more than 200 clinical images and achieve robust and precise delineations, well-suited for automated computation of orthopaedic measurements.