@MASTERSTHESIS\{IMM2005-04160,
    author       = "M. F. Hansen",
    title        = "Quality Estimation and Segmentation of Pig Backs",
    year         = "2005",
    keywords     = "The Virtual Slaughterhouse, Quality estimation of meat, Rib removal, Radial basis functions, Region based segmentation, Region of interest, Shape models, Implicit surfaces, Level sets, Coupling shape models, {CT}",
    school       = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    note         = "Supervised by Assoc. Prof. Rasmus Larsen, and Assoc. Prof. Bjarne Ersb{\o}ll, {IMM}.",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4160-full.html",
    abstract     = "This thesis explores the possibility of using {CT} scans of pork bodies to estimate the quality of the pig product “18cm back”. It presents the necessary tools for deriving the measures, which are needed to perform a quality estimation. This includes finding the ribs, extracting the 18cm back from the pork middle, sectioning the 18cm back into four parts and finding the meat-fat percentage in the 18cm back.

Pure intensity based classification is an obvious approach for determining the meat-fat percentage as the intensities in a {CT} scan is given in a relative scale (Hounsfield). The possibility of performing a meat-fat segmentation with a trained linear or quadratic classifier is examined in this thesis. However, pure intensity based classification might function poorly under the presence of intensity inhomogeneities introduced by the {CT} scanner. A small investigation was conducted in to the matter, and it revealed the presence of scanner introduced artifacts in the used data set.

As an alternative approach, the possibility of performing a shape guided segmentation of the pig back is investigated. An implicit parametric shape model is presented which does not rely on corresponding landmarks. The shape model is later integrated into a region based segmentation framework. The extensive number of muscles and the small separation between the muscles in a pig back demand for the models to be coupled. A couple of initial attempts of modelling the coupling of the models in to the region based framework is likewise presented.

The basic segmentation framework is tested and compared against an Active Appearance Model on a set of {MR} images of the Corpus Collasum, while the coupled segmentation framework is tested on a set of {CT} scans of the pork middle."
}