Automatic segmentation of abdominal fat from CT data

Amol Pednekar; Alok N. Bandekar; Ioannis A. Kakadiaris; Morteza Naghavi

doi:10.1109/ACVMOT.2005.31

Automatic segmentation of abdominal fat from CT data

Amol Pednekar, Alok N. Bandekar, Ioannis A. Kakadiaris, Morteza Naghavi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

30 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	308-315
Number of pages	8
ISBN (Print)	0769522718, 9780769522715
DOIs	https://doi.org/10.1109/ACVMOT.2005.31
State	Published - 2005
Event	7th IEEE Workshop on Applications of Computer Vision, WACV 2005 - Breckenridge, CO, United States Duration: Jan 5 2005 → Jan 7 2005

Publication series

Name	Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005

Conference

Conference	7th IEEE Workshop on Applications of Computer Vision, WACV 2005
Country/Territory	United States
City	Breckenridge, CO
Period	1/5/05 → 1/7/05

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Software

Access to Document

10.1109/ACVMOT.2005.31

Cite this

Pednekar, A., Bandekar, A. N., Kakadiaris, I. A., & Naghavi, M. (2005). Automatic segmentation of abdominal fat from CT data. In Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005 (pp. 308-315). Article 4129496 (Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACVMOT.2005.31

Automatic segmentation of abdominal fat from CT data. / Pednekar, Amol; Bandekar, Alok N.; Kakadiaris, Ioannis A. et al.
Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005. Institute of Electrical and Electronics Engineers Inc., 2005. p. 308-315 4129496 (Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Pednekar, A, Bandekar, AN, Kakadiaris, IA & Naghavi, M 2005, Automatic segmentation of abdominal fat from CT data. in Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005., 4129496, Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005, Institute of Electrical and Electronics Engineers Inc., pp. 308-315, 7th IEEE Workshop on Applications of Computer Vision, WACV 2005, Breckenridge, CO, United States, 1/5/05. https://doi.org/10.1109/ACVMOT.2005.31

Pednekar A, Bandekar AN, Kakadiaris IA, Naghavi M. Automatic segmentation of abdominal fat from CT data. In Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005. Institute of Electrical and Electronics Engineers Inc. 2005. p. 308-315. 4129496. (Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005). doi: 10.1109/ACVMOT.2005.31

@inproceedings{b8c0133c56a64792afc506d0089eb8bd,

title = "Automatic segmentation of abdominal fat from CT data",

abstract = "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.",

author = "Amol Pednekar and Bandekar, {Alok N.} and Kakadiaris, {Ioannis A.} and Morteza Naghavi",

year = "2005",

doi = "10.1109/ACVMOT.2005.31",

language = "English (US)",

isbn = "0769522718",

series = "Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "308--315",

booktitle = "Proceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005",

address = "United States",

note = "7th IEEE Workshop on Applications of Computer Vision, WACV 2005 ; Conference date: 05-01-2005 Through 07-01-2005",

}

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

UR - http://www.scopus.com/inward/citedby.url?scp=35348874230&partnerID=8YFLogxK

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 -

Automatic segmentation of abdominal fat from CT data

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this