Title :
Automatic Segmentation of Abdominal Fat from CT Data
Author :
Pednekar, Amol ; Bandekar, Alok N. ; Kakadiaris, Ioannis A. ; Naghavi, Morteza
Author_Institution :
Dept. of Comput. Sci., Univ. of Houston, Houston, TX
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 intra- observer 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.
Keywords :
biological tissues; computerised tomography; image segmentation; medical image processing; CT data; abdominal visceral fat accumulation; automatic segmentation; cardiovascular risk factor; computed tomography; magnetic resonance image; medical image; tissue segmentation; Abdomen; Attenuation; Biomedical imaging; Cardiac arrest; Cardiology; Computed tomography; Computer science; Image segmentation; Magnetic resonance; Magnetic resonance imaging;
Conference_Titel :
Application of Computer Vision, 2005. WACV/MOTIONS '05 Volume 1. Seventh IEEE Workshops on
Conference_Location :
Breckenridge, CO
Print_ISBN :
0-7695-2271-8
DOI :
10.1109/ACVMOT.2005.31