Computer-aided assessment of regional abdominal fat with food residue removal in CT

Academic Radiology

Volumn 20, Issue 11, 2013, Pages 1413-1421

  Makrogiannis, Sokratis ^a,c   Caturegli, Giorgio ^a   Davatzikos, Christos ^b   Ferrucci, Luigi ^a  

^a NATIONAL INSTITUTE ON AGING   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c DELAWARE STATE UNIVERSITY   (United States)

Author keywords

Body composition assessment; False positive reduction

Indexed keywords

ABDOMINAL FAT; ACCURACY; ARTICLE; BODY COMPOSITION; COMPUTED TOMOGRAPHY SCANNER; COMPUTER ASSISTED TOMOGRAPHY; FOOD INTAKE; INTRAABDOMINAL FAT; MACHINE LEARNING; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SUBCUTANEOUS FAT;

BODY COMPOSITION ASSESSMENT; FALSE POSITIVE REDUCTION;

ABDOMINAL FAT; ALGORITHMS; BODY COMPOSITION; FOOD; HUMANS; LONGITUDINAL STUDIES; PHANTOMS, IMAGING; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOGRAPHY, ABDOMINAL; SUBTRACTION TECHNIQUE; SUPPORT VECTOR MACHINES; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84885357878     PISSN: 10766332     EISSN: 18784046     Source Type: Journal    
DOI: 10.1016/j.acra.2013.08.007     Document Type: Article

References (30)

1. Kissebah A.H., Vydelingum N., Murray R., et al. Relation of body fat distribution to metabolic complications of obesity. J Clin Endocrinol Metabol 1982, 54:254-260.
2. Seidell J.C., Hautvast J.G., Deurenberg P. Overweight: fat distribution and health risks. Epidemiological observations. a review. Infusionstherapie 1989, 16:276-281.
3. Beydoun M.A., Lhotsky A., Wang Y., et al. Association of adiposity status and changes in early to mid-adulthood with incidence of Alzheimer's disease. Am J Epidemiol 2008, 168:1179-1189.
4. Ding J., Kritchevsky S.B., Hsu F.-C., et al. Association between non-subcutaneous adiposity and calcified coronary plaque: a substudy of the multi-ethnic study of atherosclerosis. Am J Clin Nutr 2008, 88:645-650.
5. Lee K., Lee S., Kim Y., et al. Waist circumference, dual-energy X-ray absortiometrically measured abdominal adiposity, and computed tomographically derived intra-abdominal fat area on detecting metabolic risk factors in obese women. Nutrition 2008, 24:625-631.
6. Kanaya A.M., Lindquist K., Harris T.B., et al. Total and regional adiposity and cognitive change in older adults: the health, aging and body composition (ABC) study. Arch Neurol 2009, 66:329-335.
7. Jensen M.D., Kanaley J.A., Reed J.E., et al. Measurement of abdominal andvisceral fat with computed tomography and dual-energy x-ray absorptiometry. Am J Clin Nutr 1995, 61:274-278.
8. Yoshizumi T., Nakamura T., Yamane M., et al. Abdominal fat: standardized technique for measurement at CT. Radiology 1999, 211:283-286.
9. Bandekar A., Naghavi M., Kakadiaris I. Performance evaluation of abdominal fat burden quantification in CT. Conf Proc IEEE Eng Med Biol Soc 2005, 3:3280-3283.
10. Chung H., Cobzas D., Birdsell L., et al. Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis. Proc SPIE 2009, 7261. 72610K.
11. Zhao B., Colville J., Kalaigian J., et al. Automated quantification of body fatdistribution on volumetric computed tomography. J Comput Assist Tomogr 2006, 30:777-783.
12. Bezdek J.C. Pattern recognition with fuzzy objective function algorithms 1981, Kluwer Academic Publishers, Norwell, MA.
13. Xu C., Prince J.L. Snakes, shapes, and gradient vector flow. IEEE Trans Image Proc 1998, 7:359-369.
14. Unser M., Aldroubi A. A review of wavelets in biomedical applications. Proc IEEE 1996, 84:626-638.
15. Daugman J. Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Trans Acoustics. Speech Signal Proc 1988, 36:1169-1179.
16. Manjunath B., Ma W. Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Machine Intell 1996, 18:837-842.
17. Lee T.S. Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Machine Intell 1996, 18:959-971.
18. Shapiro L.G., Stockman G.C. Computer vision 2001, Prentice Hall.
19. Iijima T. Basic theory on normalization of a pattern (in case of typical one-dimensional pattern). Bull Electrical Lab 1962, 26:368-388. [in Japanese].
20. Witkin A. Scale-space filtering. Int Jt Conf Artificial Intell 1983, 1019-1022.
21. Koenderink J.J. The structure of images. Biol Cybernetics 1984, 50:363-370.
22. Makrogiannis S., Vanhamel I., Fotopoulos S., et al. A watershed-based multiscale segmentation method for color images using automatic scale selection. SPIE J Electronic Imaging 2005, 14.
23. Alpert N.M., Bradshaw J.F., Kennedy D., et al. The principal axes transformation-a method for image registration. J Nucl Med 1990, 31:1717-1722.
24. Arata L.K., Dhawan A.P., Broderick J.P., et al. Three-dimensional anatomical model-based segmentation of MR brain images through Principal Axes Registration. IEEE Trans Biomed Eng 1995, 42:1069-1078.
25. Duda R.O., Hart P.E., Stork D.G. Pattern classification 2000, Wiley-Interscience Publication.
26. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discovery 1998, 2:121-167.
27. Cortes C., Vapnik V. Support-vector networks. Mach Learn 1995, 20:273-297.
28. Chang CC, Lin CJ. LIBSVM: a library for support vector machines 2006. Software available at:. http://www.csie.ntu.edu.tw/%7Ecjlin/libsvm.
29. Tang Y., Zhang Y.Q., Chawla N.V., et al. SVMs modeling for highly imbalanced classification. IEEE Trans Syst Man Cybernetics, Part B: Cybernetics 2009, 39:281-288.
30. Dice L.R. Measures of the amount of ecologic association between species. Ecology 1945, 26:297-302.