Automatic Segmentation of Abdominal Adipose Tissue in MRI

  • Thomas Hammershaimb Mosbech
  • Kasper Pilgaard
  • Allan Vaag
  • Rasmus Larsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


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 dual-emission X-ray absorptiometry (R 2 = 0.86).


Image processing MRI Abdominal adipose tissue Bias field correction Tissue classification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thomas Hammershaimb Mosbech
    • 1
  • Kasper Pilgaard
    • 2
  • Allan Vaag
    • 2
  • Rasmus Larsen
    • 1
  1. 1.DTU InformaticsTechnical University of DenmarkLyngbyDenmark
  2. 2.Steno Diabetes CenterGentofteDenmark

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