Accurate fully automatic femur segmentation in pelvic radiographs using regression voting.

C. Lindner, S. Thiagarajah, J. M. Wilkinson, G. A. Wallis, Timothy F. Cootes, arcOGEN Consortium

    Research output: Contribution to journalArticlepeer-review


    Extraction of bone contours from radiographs plays an important role in disease diagnosis, pre-operative planning, and treatment analysis. We present a fully automatic method to accurately segment the proximal femur in anteroposterior pelvic radiographs. A number of candidate positions are produced by a global search with a detector. Each is then refined using a statistical shape model together with local detectors for each model point. Both global and local models use Random Forest regression to vote for the optimal positions, leading to robust and accurate results. The performance of the system is evaluated using a set of 519 images. We show that the fully automated system is able to achieve a mean point-to-curve error of less than 1 mm for 98% of all 519 images. To the best of our knowledge, this is the most accurate automatic method for segmenting the proximal femur in radiographs yet reported.
    Original languageEnglish
    Pages (from-to)353-360
    Number of pages7
    JournalMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    Issue number3
    Publication statusPublished - 2012


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