@CONFERENCE\{IMM2011-06008,
    author       = "J. L. Skytte and A. L. Dahl and R. Larsen and L. B. Christensen and B. K. Ersb{\o}ll",
    title        = "Classification Methods for {CT-}Scanned Carcass Midsections - A Study of Noise Stability",
    year         = "2011",
    month        = "may",
    keywords     = "Fat segmentation, {CT-}scan, Food processing, Segmentation of noisy data",
    booktitle    = "Scandinavian Workshop on Imaging Food Quality, in conjunction with Scandinavian Conference of Image Analysis, Sweden, May 2011",
    volume       = "",
    series       = "",
    editor       = "Proceedings of Scandinavian Workshop on Imaging Food Quality",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6008-full.html",
    abstract     = "Computed tomography (CT) has successfully been applied in medical environments for decades. In recent years {CT} has also made its entry to the industrial environments, including the slaughter houses. In this paper we investigate classification methods for an online {CT} system, in order to assist in the segmentation of the outer fat layer in the mid- section of {CT-}scanned pig carcasses. Prior information about the carcass composition can potentially be applied for a fully automated solution, in order to optimize the slaughter line. The methods comprises Markov Random Field and contextual Bayesian classification, and are adapted to use neighbourhood information in {2D} and {3D}. Artificial Poisson noise is added to the provided dataset to determine how well each of the meth- ods handles noise. Good noise handling will allow for scanning at lower energies. The investigated methods did not perform better than the refer- ence model in terms of classification, but the {MRF} segmentation showed promising results in a case with extreme simulated noise."
}