Automatic finger joint detection for volumetric hand imaging

Johannes Bopp, Mathias Unberath, Stefan Steidl, Rebecca Fahrig, Isabelle Oliveira, Arnd Kleyer, Andreas Maier

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose a fully automatic method for robust finger joint detection in T1 weighted magnetic resonance imaging (MRI) sequences for initialization of statistical shape model (SSM) based segmentation. We propose a robust method that only relies on few training samples. Therefore, a parallel-beam forward projection is calculated on the MRI volume. A trained Bagging classifier will detect the joints in 2D which are then splatted into the 3D volume. For evaluation, leave-one-out cross validation was performed. The detection of the joints in 2D yielded a Dice score of 0.67 ± 0.056 with respect to a manually obtained ground truth. For the initialization of SSM-based segmentation algorithms, the results are very promising.

Original languageEnglish (US)
Title of host publicationBildverarbeitung fur die Medizin 2016
Subtitle of host publicationAlgorithmen – Systeme – Anwendungen - Proceedings des Workshops
EditorsThomas M. Deserno, Heinz Handels, Thomas Tolxdorff, Hans-Peter Meinzer
PublisherKluwer Academic Publishers
Number of pages6
ISBN (Print)9783662494646
StatePublished - 2017
Externally publishedYes
EventWorkshops on Image processing for the medicine, 2016 - Berlin, Germany
Duration: Mar 13 2016Mar 15 2016

Publication series

NameInformatik aktuell
ISSN (Print)1431-472X


ConferenceWorkshops on Image processing for the medicine, 2016

ASJC Scopus subject areas

  • Modeling and Simulation


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