Fully automatic segmentation of the proximal femur using random forest regression voting

C. Lindner, S. Thiagarajah, J. M. Wilkinson, G. A. Wallis, T. F. Cootes

    Research output: Contribution to journalArticlepeer-review


    Extraction of bone contours from radiographs plays an important role in disease diagnosis, preoperative 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 839 images of mixed quality. We show that the local search significantly outperforms a range of alternative matching techniques, and that the fully automated system is able to achieve a mean point-to-curve error of less than 0.9 mm for 99% of all 839 images. To the best of our knowledge, this is the most accurate automatic method for segmenting the proximal femur in radiographs yet reported. © 1982-2012 IEEE.
    Original languageEnglish
    Article number6497663
    Pages (from-to)1462-1472
    Number of pages10
    JournalIEEE Transactions on Medical Imaging
    Issue number8
    Publication statusPublished - 2013


    • Automatic femur segmentation
    • Constrained Local Models (CLMs)
    • femur detection
    • Hough transform
    • Random Forests


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