@inproceedings{aa409a905130406e961dc978b84c6c72,
title = "Automatic segmentation of abdominal adipose tissue in MRI",
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 dual-emission X-ray absorptiometry (R 2 = 0.86).",
keywords = "Abdominal adipose tissue, Bias field correction, Image processing, MRI, Tissue classification",
author = "Mosbech, {Thomas Hammershaimb} and Kasper Pilgaard and Allan Vaag and Rasmus Larsen",
year = "2011",
month = may,
day = "30",
doi = "10.1007/978-3-642-21227-7_47",
language = "English",
isbn = "9783642212260",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "501--511",
booktitle = "Image Analysis - 17th Scandinavian Conference, SCIA 2011, Proceedings",
note = "17th Scandinavian Conference on Image Analysis, SCIA 2011 ; Conference date: 23-05-2011 Through 27-05-2011",
}