Development of Three Dimensional Automatic Body Fat Measurement Software from CT, and Its Validation and Evaluation


Abdominal obesity describes the accumulation of excessive fat in the abdomen. It is known that depending on its distribution, visceral obesity presents a greater danger to health than subcutaneous obesity. To properly prevent and treat visceral obesity, accurate evaluation methods are necessary, and hence quantitative VAT estimation is extremely important. CT scans are the most accurate method for estimating VAT, but it requires a great deal of time and effort, limiting its use in studying or evaluating obesity in patients. This paper proposed automatic measurement software that could quickly differentiate between and measure VAT and SAT. The method was verified using a total of 100 abdominal CT data values; this paper measured the SAT and VAT in the entire abdomen using the automatic measurement software. Additionally, through a comparative evaluation between the automated measurements and manual measurements such as BMI and waist circumference, clinical reliability and viability were validated and evaluated. Between automated measurements and manual measurements, the TAT (r = 0.995, p = 0.01), SAT (r = 0.987, p = 0.01) and VAT (r = 0.993, p = 0.01) showed high correlation. Using BMI as the main metric, the TAT for automated measurements (r = 0.674, p = 0.01) and the TAT for manual measurements (r = 0.703, p = 0.01) showed the strongest correlation. When using waist circumference, the VAT for automated measurements (r = 0.826, p = 0.01) and the VAT for manual measurements (r = 0.822, p = 0.01) showed the strongest correlation. With these results, the reliability and viability of the automatic measurement software were confirmed. The software is expected to help greatly in reducing the time and in providing objective data of VAT measurements from CT scans for clinical research.

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Kim, Y. , Jeong, J. , Nam, S. , Kim, M. , Oh, J. , Kim, K. and Sohn, D. (2015) Development of Three Dimensional Automatic Body Fat Measurement Software from CT, and Its Validation and Evaluation. Journal of Biomedical Science and Engineering, 8, 665-673. doi: 10.4236/jbise.2015.810063.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Ford, E., Li, C., Zhao, G. and Tsai, J. (2011) Trends in Obesity and Abdominal Obesity among Adults in the United States from 1999-2008. International Journal of Obesity, 35, 736-743. h
[2] Després, J.P. (1991) Obesity and Lipid Metabolism: Relevance of Body Fat Distribution. Current Opinion in Lipidology, 2, 5-15.
[3] Fujioka, S., Matsuzawa, Y., Tokunaga, K. and Tarui, S. (1987) Contribution of Intra-Abdominal Fat Accumulation to the Impairment of Glucose and Lipid Metabolism in Human Obesity. Metabolism-Clinical and Experimental, 36, 54-59.
[4] Zamboni, M., Armellini, F., Sheiban, I., De Marchi, M., Todesco, T., Bergamo-Andreis, I., Cominacini, L. and Bosello, O. (1992) Relation of Body Fat Distribution in Men and De-gree of Coronary Narrowings in Coronary Artery Disease. The American Journal of Cardiology, 70, 1135-1138.
[5] Jackson, A., Pollock, M.L., Graves, J.E. and Mahar, M. (1988) Reliability and Validity of Bioelectrical Impedance in Determining Body Composition. Journal of Applied Physiology, 64, 529-534.
[6] Donnelly, L.F., O’Brien, K.J., Dardzinski, B.J., Poe, S.A., Bean, J.A., Holland, S.K. and Daniels, S.R. (2003) Using a Phantom to Compare MR Techniques for Determining the Ratio of Intra-Abdominal to Subcutaneous Adipose Tissue. American Journal of Roentgenology, 180, 993-998.
[7] Seidell, J.C., Bakker, C. and van der Kooy, K. (1990) Imaging Techniques for Measuring Adipose-Tissue Distribution—A Comparison between Computed Tomography and 1.5-T Magnetic Resonance. The American Journal of Clinical Nutrition, 51, 953-957.
[8] Tokunaga, K., Matsuzawa, Y., Ishikawa, K. and Tarui, S. (1982) A Novel Technique for the Determination of Body Fat by Computed Tomography. International Journal of Obesity, 7, 437-445.
[9] Yoshizumi, T., Nakamura, T., Yamane, M., Islam, A.H.M.W., Menju, M., Yamasaki, K., Arai, T., Kotani, K., Funahashi, T. and Yamashita, S. (1999) Ab-dominal Fat: Standardized Technique for Measurement at CT1. Radiology, 211, 283-286.
[10] Nam, S.Y., Choi, I.J., Ryu, K.H., Park, B.J., Kim, H.B. and Nam, B.H. (2010) Abdominal Visceral Adipose Tissue Volume Is Associated with Increased Risk of Erosive Esophagitis in Men and Women. Gastroenterology, 139, 1902-1911.
[11] Garrouste-Orgeas, M., Troché, G., Azoulay, E., Caubel, A., Lassence, A., Cheval, C., Montesino, L., Thuong, M., Vincent, F. and Cohen, Y. (2004) Body Mass Index. Intensive Care Medicine, 30, 437-443.
[12] Enzi, G., Gasparo, M., Biondetti, P.R., Fiore, D., Semisa, M. and Zurlo, F. (1986) Subcutaneous and Visceral Fat Distribution According to Sex, Age, and Overweight, Evaluated by Computed Tomography. The American Journal of Clinical Nutrition, 44, 739-746.
[13] Macor, C., Ruggeri, A., Mazzonetto, P., Federspil, G., Cobelli, C. and Vettor, R. (1997) Visceral Adipose Tissue Impairs Insulin Secretion and Insulin Sensitivity but Not Energy Expenditure in Obesity. Metabolism: Clinical and Experimental, 46, 123-129.
[14] Han, T., Van Leer, E., Seidell, J. and Lean, M. (1995) Waist Circumference Action Levels in the Identification of Cardiovascular Risk Factors: Prevalence Study in a Random Sample. BMJ, 311, 1401-1405.
[15] Ashwell, M., Cole, T.J. and Dixon, A.K. (1985) Obesity: New Insight into the Anthropometric Classification of Fat Distribution Shown by Computed Tomography. British Medical Journal, 290, 1692-1694.
[16] Kim, C.H. and Jung, J.I. (2006) Study for Hounsfield Units in Computed Tomogram with Jaw Lesion. Journal of the Korean Association of Oral and Maxillofacial Surgeons, 32, 391-396.
[17] Chang, F., Chen, C.J. and Lu, C.J. (2004) A Linear-Time Component-Labeling Algorithm Using Contour Tracing Technique. Computer Vision and Image Understanding, 93, 206-220.
[18] Di Stefano, L. and Bulgarelli, A. (1999) A Simple and Efficient Connected Components Labeling Algorithm. IEEE: Image Analysis and Processing, Venice, 27-29 September 1999, 322-327.
[19] Bandekar, A.N., Naghavi, M. and Kakadiaris, I.A. (2006) Automated Pericardial Fat Quantification in CT Data. IEEE-EMBS 2006. 28th Annual International Conference of the IEEE, New York, 30 August-3 September 2006, 932- 935.
[20] Graham, R.L. and Frances Yao, F. (1983) Finding the Convex Hull of a Simple Polygon. Journal of Algorithms, 4, 324-331.
[21] Benesty, J., Chen, J., Huang, Y. and Cohen, I. (2009) Pearson Correlation Coefficient. In: Noise Reduction in Speech Processing, Springer, Heidelberg, 37-40.
[22] Martin Bland, J. and Altman, D. (1986) Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement. The Lancet, 327, 307-310.
[23] Bland, J.M. and Altman, D.G. (1999) Measuring Agreement in Method Comparison Studies. Statistical Methods in Medical Research, 8, 135-160.
[24] Doi, K. (2005) Current Status and Future Potential of Computer-Aided Diagnosis in Medical Imaging. British Journal of Radiology, 78, s3-s19.
[25] Bandekar, A.N., Naghavi, M. and Kakadiaris, I.A. (2005) Performance Evaluation of Abdominal Fat Burden Quantification in CT. IEEE-EMBS 2005. 27th Annual International Conference of the IEEE, Shanghai, 17-18 January 2005, 3280-3283.
[26] Kullberg, J., Ahlstrom, H., Johansson, L. and Frimmel, H. (2007) Automated and Reproducible Segmentation of Visceral and Subcutaneous Adipose Tissue from Abdominal MRI. International Journal of Obesity, 31, 1806-1817.
[27] Kim, Y.J., Lee, S.H., Kim, T.Y., Park, J.Y., Choi, S.H. and Kim, K.G. (2013) Body Fat Assessment Method Using CT Images with Separation Mask Algorithm. Journal of Digital Imaging, 26, 155-162.

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