Segmenting multiple objects with overlapping appearance and uncertainty

Matthias Seise, Stephen McKenna, Ian Ricketts, Carlos Wigderowitz

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

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    Abstract

    A probabilistic method is proposed for segmentation of multiple objects that overlap or are in close proximity to one another. 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 method is described in detail for the problem of segmenting femur and tibia in x-ray images. The result is a probabilistic segmentation that quantifies uncertainty so that measurements such as joint space can be made with associated uncertainty.
    Original languageEnglish
    Title of host publicationBritish Machine Vision Conference
    PublisherBritish Machine Vision Association and Society for Pattern Recognition
    Pages839-848
    Number of pages10
    DOIs
    Publication statusPublished - 2006

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