Automatic Segmentation of Abdominal Adipose Tissue in MRI

Thomas Hammershaimb Mosbech, Kasper Pilgaard, Allan Vaag, Rasmus Larsen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    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).
    Original languageEnglish
    Title of host publicationImage Analysis : 17th Scandinavian Conference, SCIA 2011 - Ystad, Sweden, May 2011 - Proceedings
    Publication date2011
    ISBN (Print)978-3-642-21226-0
    ISBN (Electronic)978-3-642-21227-7
    Publication statusPublished - 2011
    Event17th Scandinavian Conference on Image Analysis (SCIA) - Ystad Saltsjöbad, Ystad, Sweden
    Duration: 23 May 201127 May 2011


    Conference17th Scandinavian Conference on Image Analysis (SCIA)
    LocationYstad Saltsjöbad
    Internet address
    SeriesLecture Notes in Computer Science


    • Tissue classification
    • Abdominal adipose tissue
    • MRI
    • Bias field correction
    • Image processing


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