Probabilistic segmentation of the knee joint from x-ray images

Matthias Seise, Stephen McKenna, Ian Ricketts, Carlos Wigderowitz

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    Abstract

    A probabilistic method is proposed for segmentation of the knee joint. A likelihood function is formulated
    that explicitly models overlapping object appearance. Priors on global appearance and geometry (including shape)
    are learned from example images. Markov chain Monte Carlo methods are used to obtain samples from a posterior
    distribution over model parameters from which expectations can be estimated. The result is a probabilistic segmentation
    that quantifies uncertainty so that measurements such as joint space can be made with associated uncertainty.
    Joint space area and mean point-to-contour distance are used for evaluation.
    Original languageEnglish
    Title of host publicationMedical Image Understanding and Analysis
    PublisherBritish Machine Vision Association and Society for Pattern Recognition
    Pages110-114
    Number of pages5
    Publication statusPublished - 2006

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  • Cite this

    Seise, M., McKenna, S., Ricketts, I., & Wigderowitz, C. (2006). Probabilistic segmentation of the knee joint from x-ray images. In Medical Image Understanding and Analysis (pp. 110-114). British Machine Vision Association and Society for Pattern Recognition. http://www.researchgate.net/publication/250702501_Probabilistic_Segmentation_of_the_Knee_Joint_from_X-ray_Images