TY - GEN
T1 - Automatic segmentation of abdominal fat from CT data
AU - Pednekar, Amol
AU - Bandekar, Alok N.
AU - Kakadiaris, Ioannis A.
AU - Naghavi, Morteza
PY - 2005
Y1 - 2005
N2 - Abdominal visceral fat accumulation is one of the most important cardiovascular risk factors. Currently, Computed Tomography and Magnetic Resonance images are manually segmented to quantify abdominal fat distribution. The manual, delineation of subcutaneous and visceral fat is labor intensive, time consuming, and subject to inter- and intraobserver variability. An automatic segmentation method would eliminate intra- and inter-observer variability and provide more consistent results. In this paper, we present a hierarchical, multi-class, multi-feature, fuzzy affinity-based computational framework for tissue segmentation in medical images. We have applied this framework for automatic segmentation of abdominal fat. An evaluation of the accuracy of our method indicates bias and limits of agreement comparable to the inter-observer variability inherent in manual segmentation.
AB - Abdominal visceral fat accumulation is one of the most important cardiovascular risk factors. Currently, Computed Tomography and Magnetic Resonance images are manually segmented to quantify abdominal fat distribution. The manual, delineation of subcutaneous and visceral fat is labor intensive, time consuming, and subject to inter- and intraobserver variability. An automatic segmentation method would eliminate intra- and inter-observer variability and provide more consistent results. In this paper, we present a hierarchical, multi-class, multi-feature, fuzzy affinity-based computational framework for tissue segmentation in medical images. We have applied this framework for automatic segmentation of abdominal fat. An evaluation of the accuracy of our method indicates bias and limits of agreement comparable to the inter-observer variability inherent in manual segmentation.
UR - http://www.scopus.com/inward/record.url?scp=35348874230&partnerID=8YFLogxK
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U2 - 10.1109/ACVMOT.2005.31
DO - 10.1109/ACVMOT.2005.31
M3 - Conference contribution
AN - SCOPUS:35348874230
SN - 0769522718
SN - 9780769522715
T3 - Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005
SP - 308
EP - 315
BT - Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Workshop on Applications of Computer Vision, WACV 2005
Y2 - 5 January 2005 through 7 January 2005
ER -