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

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

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    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
    Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|Med Image Comput Comput Assist Interv
    PublisherSpringer Nature
    Pages353-360
    Number of pages7
    Volume15
    Publication statusPublished - 2012
    EventInternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) - Nice, France
    Duration: 1 Jan 1824 → …

    Publication series

    NameLecture Notes in Computer Science

    Conference

    ConferenceInternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
    CityNice, France
    Period1/01/24 → …

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