@CONFERENCE\{IMM2011-06053,
    author       = "T. H. Mosbech and K. A. Pilgaard and A. Vaag and R. Larsen",
    title        = "Automatic Segmentation of Abdominal Adipose Tissue in {MRI}",
    year         = "2011",
    pages        = "501-511",
    booktitle    = "Proceedings on the Scandinavian Conference on Image Analysis, Ystad, Sweden",
    volume       = "",
    series       = "Lecture Notes in Computer Science",
    editor       = "",
    publisher    = "Springer",
    organization = "",
    address      = "",
    note         = "{DOI} 10.1007/978-3-642-21227-7",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6053-full.html",
    abstract     = "This paper presents a method for automatically segmenting abdominal adipose tissue from {3-}dimensional magnetic resonance images. We distinguish between three types of adipose tissue; visceral, deep subcutaneous and superficial subcutaneous. Images are pre-processed to remove the bias field effect of intensity in-homogeneities. This effect is estimated by a thin plate spline extended to fit two classes of automatically sampled intensity points in {3D}. Adipose tissue pixels are labelled with fuzzy c-means clustering and locally determined thresholds. The visceral and subcutaneous adipose tissue are separated using deformable models, incorporating information from the clustering. The subcutaneous adipose tissue is subdivided into a deep and superficial part by means of dynamic programming applied to a spatial transformation of the image data. Regression analysis shows good correspondences between our results and total abdominal adipose tissue percentages assessed by dualemission {X-}ray absorptiometry (R2 = 0.86)."
}