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).
Original language | English |
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Title of host publication | Image Analysis : 17th Scandinavian Conference, SCIA 2011 - Ystad, Sweden, May 2011 - Proceedings |
Publisher | Springer |
Publication date | 2011 |
Pages | 501-511 |
ISBN (Print) | 978-3-642-21226-0 |
ISBN (Electronic) | 978-3-642-21227-7 |
DOIs | |
Publication status | Published - 2011 |
Event | 17th Scandinavian Conference on Image Analysis (SCIA) - Ystad Saltsjöbad, Ystad, Sweden Duration: 23 May 2011 → 27 May 2011 http://www.maths.lth.se/vision/scia2011/ |
Conference
Conference | 17th Scandinavian Conference on Image Analysis (SCIA) |
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Location | Ystad Saltsjöbad |
Country/Territory | Sweden |
City | Ystad |
Period | 23/05/2011 → 27/05/2011 |
Internet address |
Series | Lecture Notes in Computer Science |
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Number | 6688 |
ISSN | 0302-9743 |
Keywords
- Tissue classification
- Abdominal adipose tissue
- MRI
- Bias field correction
- Image processing