Conference paper
Automatic Segmentation of Abdominal Adipose Tissue in MRI
Department of Informatics and Mathematical Modeling, Technical University of Denmark1
DTU Data Analysis, Department of Informatics and Mathematical Modeling, Technical University of Denmark2
Steno Diabetes Centre3
Image Analysis and Computer Graphics, Department of Informatics and Mathematical Modeling, Technical University of Denmark4
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).
Language: | English |
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Publisher: | Springer |
Year: | 2011 |
Pages: | 501-511 |
Proceedings: | 17th Scandinavian Conference on Image Analysis (SCIA) |
Series: | Lecture Notes in Computer Science |
Journal subtitle: | 17th Scandinavian Conference, Scia 2011 - Ystad, Sweden, May 2011 - Proceedings |
ISBN: | 3642212263 , 3642212271 , 9783642212260 and 9783642212277 |
ISSN: | 16113349 and 03029743 |
Types: | Conference paper |
DOI: | 10.1007/978-3-642-21227-7_47 |
ORCIDs: | Larsen, Rasmus |