Parts-based segmentation with overlapping part models using Markov chain Monte Carlo

Matthias Seise, Stephen J. McKenna, Ian W. Ricketts, Carlos A. Wigderowitz

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    A probabilistic method is proposed for segmenting 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, conditioned upon the model, so that measurements such as joint space can be made with associated uncertainty. (C) 2008 Elsevier B.V. All rights reserved.

    Original languageEnglish
    Pages (from-to)504-513
    Number of pages10
    JournalImage and Vision Computing
    Volume27
    Issue number5
    DOIs
    Publication statusPublished - 2 Apr 2009

    Keywords

    • Probabilistic segmentation
    • Model-based segmentation
    • Markov chain Monte Carlo
    • ACTIVE SHAPE MODELS
    • KNEE OSTEOARTHRITIS
    • OBJECTS
    • ATLAS
    • IMAGE

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