TY - GEN
T1 - Automatic segmentation of abdominal adipose tissue in MRI
AU - Mosbech, Thomas Hammershaimb
AU - Pilgaard, Kasper
AU - Vaag, Allan
AU - Larsen, Rasmus
PY - 2011/5/30
Y1 - 2011/5/30
N2 - 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).
AB - 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).
KW - Abdominal adipose tissue
KW - Bias field correction
KW - Image processing
KW - MRI
KW - Tissue classification
UR - http://www.scopus.com/inward/record.url?scp=79957505759&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21227-7_47
DO - 10.1007/978-3-642-21227-7_47
M3 - Article in proceedings
AN - SCOPUS:79957505759
SN - 9783642212260
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 501
EP - 511
BT - Image Analysis - 17th Scandinavian Conference, SCIA 2011, Proceedings
T2 - 17th Scandinavian Conference on Image Analysis, SCIA 2011
Y2 - 23 May 2011 through 27 May 2011
ER -