根據目前肥胖相關的研究，環繞於主動脈的脂肪體積，對於評估心血管疾病的風險有很大的影響，而要做脂肪體積的量測，在許多臨床研究中，常使用3-D醫學影像來測驗，並藉由影像處理技術的運用，以設定影像強度值來擷取主動脈以及脂肪組織等部位的方法，進一步地獲取感興趣的區域。然而，使用傳統二元分割法計算的體積與實際物體的體積常會因為醫學儀器掃描的處理，而產生誤差。為了解決這個問題，我們提出了一套自動化量測脂肪體積的演算法，可用於解決主動脈周圍區域中所產生的部分體積效應，讓量測的脂肪體積更加準確。此方法包含主動輪廓模型、基因演算法和模糊C均值演算法三種技術於影像切割的部分，另外也提出了以部分體積校正為概念的方法，量測上述方法標示出來的脂肪體積。我們使用30位病患的電腦斷層影像以及藉由專家確認的手動標示圖來評估所提方法在於計算脂肪體積與其他方法的差異。實驗結果，此方法在準確性有顯著提升，而且在重現性也有良好的效果。

In the research of obesity, the volume of adipose tissue surrounding the aorta plays an essential role in evaluation of atherosclerotic cardiovascular disease (CVD). In many clinical studies, the volume measurement of adipose tissue often uses 3D medical images. Additionally, image processing techniques such as segmentation are used to retrieve the aorta and adipose tissue; the region of interest (ROI) is then acquired from the segmented results. However, the results calculated using conventional binary segmentation and voxel-count method usually differ from the actual volume of the object because of the medical instrument scanning process. In order to solve this problem, we provide an automatic algorithm for measuring the volume of adipose tissue in ROI. The algorithm resolves the problem of the partial volume effect (PVE) which emerges in the region around the boundary of the segmented results, thus improving accuracy. The proposed method contains active contour models (ACMs), genetic algorithm (GA) and fuzzy C-means (FCM) clustering algorithm for segmentation and utilizes partial volume correction-based method for quantification of volume from the previously segmented results. We provide computed tomography (CT) images from 30 patients and the ground truth of adipose tissue which were checked by a radiologist to evaluate the measurement of adipose tissue volume by the proposed and other methods. The experimental results show this method improved accuracy significantly and has high reproducibility.

[1] P. Poirier, T. D. Giles, G. A. Bray, Y. Hong, J. S. Stern, F. X. Pi-Sunyer and R. E. Eckel, “Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss,” AHA Scientific Statement, pp. 898-918, 2006.
[2] M. Bastien, P. Poirier, I. Lemieux and J. P. Després, “Overview of epidemiology and contribution of obesity to cardiovascular disease,” Progress in Cardiovascular Diseases, vol. 56, no. 4, pp. 369-381, 2014.
[3] Y. Lu, K. Hajifathalian, M. Ezzati, M. Woodward, E. B. Rimm and G. Danaei, “Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1·8 million participants,” Lancet, pp. 970-983, 2014.
[4] R. D. Abbott, G. R. Behrens, D. S. Sharp, B. L. Rodriguez, C. M. Burchfiel, G. W. Ross, K. Yano and J. D. Curb, “Body mass index and thromboembolic stroke in nonsmoking men in older middle age,” Stroke, vol. 25 ,no. 12, pp. 2370-2376, 1994.
[5] Y. Jiang, Y. Chen, D. Manuel, H. Morrison, Y. Mao and Obesity Working Group, “Quantifying the impact of obesity category on major chronic diseases in Canada,” The Scientific World Journal, vol. 17, no. 7, pp.1211–1221.
[6] J. He, L. G. Ogden, L. A. Bazzano, S. Vupputuri, C. Loria and P. K. Whelton, “Risk factors for congestive heart failure in US men and women: NHANES I epidemiologic follow-up study,” Archives of Internal Medicine, vol. 161, no. 7, pp. 996-1002, 2001.
[7] S. Kenchaiah, J. C. Evans, D. Levy, P. W. Wilson, E. J. Benjamin, M. G. Larson, W. B. Kannel and R. S. Vasan, “Obesity and the risk of heart failure,” The New England Journal of Medicine, vol. 347, no. 5, pp. 305-313, 2002.
[8] K. M. Scott, M. A. McGee, J. E. Wells and M. A. Oakley Browne, “Obesity and mental disorders in the adult general population,” Journal of Psychosomatic Research, vol. 64, no. 1, pp. 97-105, 2008.
[9] R. Taguchi, J. Takasu, Y. Itani, R. Yamamoto, K. Yokoyama, S. Watanabe and Y. Masuda, “Pericardial fat accumulation in men as a risk factor for coronary artery disease,” Atherosclerosis, vol. 157, no. 1, pp. 203-209, 2001.
[10] B. J. Wheeler1, N. Patterson, D. R. Love, D. Prosser, P. Tomlinson, B. J. Taylor and P. Manning, “Frequency and genetic spectrum of maturity-onset diabetes of the young (MODY) in southern New Zealand,” J Diabetes Metab Disord., vol. 12, no. 1, pp. 12-46, 2013.
[11] W. L. Taylor and A. R. Behnke, “Anthropometric comparison of muscular and obese men,” Journal of Applied Physiology, pp. 955–959, 1961.
[12] P. A. Fowler, M. F. Fuller, C. A. Glasbey, G. G. Cameron and M. A. Foster, “Validation of the in vivo measurement of adipose tissue by magnetic resonance imaging of lean and obese pigs,” The American Journal of Clinical Nutrition, vol. 56, no. 1, pp. 7-13, 1992.
[13] K. H. Liu, Y. L. Chan, J. C. Chan, W. B. Chan, M. O. Kong and M. Y. Poon, “The preferred magnetic resonance imaging planes in quantifying visceral adipose tissue and evaluating cardiovascular risk,” Diabetes, Obesity & Metabolism, vol. 7, no. 5, pp. 547-554, 2005.
[14] A. Pietrobelli, A. L. Boner and L. Tato, “Adipose tissue and metabolic effects: new insight into measurements,” International Journal of Obesity, pp.97-100, 2005.
[15] P. Maurovich-Horvat, J. Massaro, C. S. Fox, F. Moselewski, C. J. O’Donnell and U. Hoffmann, “Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomography,” International Journal of Obesity, vol. 31, no. 3, pp. 500-506, 2005.
[16] S. Abbara, J. C. Desai, R. C. Cury, J. Butler, K. Nieman and V. Reddy, “Mapping epicardial fat with multidetector computed tomography to facilitate percutaneous transepicardial arrhythmia ablation,” European Journal of Radiology, vol. 57, no. 3, pp. 417-722, 2006.
[17] G. Iacobellis, F. Assael, M. C. Ribaudo, A. Zappaterreno, G. Alessi, U. D. Mario and F. Leonetti, “Epicardial fat from echocardiography: a new method for visceral adipose tissue prediction,” Obesity Research, vol. 11, no. 2, pp. 304-310, 2003.
[18] C. L. Schlett, J. M. Massaro, S. J. Lehman, F. Bamberg, C. J. O’Donnell, C. S. Fox and U. Hoffmann, “Novel measurements of periaortic adipose tissue in comparison to anthropometric measures of obesity, and abdominal adipose tissue,”. Int J Obes., vol. 33, no. 2, pp. 226-232, 2009.
[19] Z. Kaya, S. Ulucan, K. Mehmet, O. Akyurek, H. Katlandur, A. Keser, D. Efe, H. Ozdil and M. S. Ulgen, “The association between thoracic periaortic fat and major adverse cardiovascular events,” Wien Klin Wochenschr, vol. 127, no. 5-6, pp. 191-196, 2015.
[20] T. Yoshizumi, T. Nakamura, M. Yamane, A. H. Islam, M. Menju, K. Yamasaki, T. Arai, K. Kotani, T. Funahashi, S. Yamashita and Y. Matsuzawa, “Abdominal fat: Standardized technique for measurement at CT,” Radiology, vol. 211, pp. 283–286, 1999.
[21] D. Val-Laillet, S. Blat, I. Louveau and C. H. Malbert, “A computed tomography scan application to evaluate adiposity in a minipig model of human obesity,” Br J Nutr., vol. 104, no. 11, pp. 1719-1728, 2010.
[22] R. Daniel, C. Juan and M. Alejandro, “Quantification of subcutaneous and visceral adipose tissue using CT,” Medical Measurement and Application, pp.20-21, 2006.
[23] C. S. Fox, J. M. Massaro, U. Hoffmann, K. M. Pou, P. Maurovich-Horvat, C. Y. Liu, R. S. Vasan, J. M. Murabito, J. B. Meigs, L. A. Cupples, R. B. D'Agostino and C. J. O'Donnell, “Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study,” Circulation, vol. 116, no. 1, pp. 39-48, 2007.
[24] L. Sjostrom, H. Kvist, A. Cederblad and U. Tylen, “Determination of total adipose tissue and body fat in women by computed tomography, 40K, and tritium,” The American Journal of Physiology, pp. 736-745, 1986.
[25] H. Kvist, B. Chowdhury, L. Sjostrom, U. Tylen and A.Cederblad, “Adipose tissue volume determination in males by computed tomography and 40K,” International Journal of Obesity, vol. 12, no. 3, pp. 249-266, 1988.
[26] D. L. Pham, C. Xu and J. L. Prince, “Current methods in medical image segmentation,” Annu. Rev. Biomed. Eng., vol. 2, no. 1, pp. 315-337, 2000.
[27] D. J. Withey and Z. J. Koles, “Medical image segmentation: Methods and software,” Proc. Int. Symp. Noninvasive Funct. Source Imag. Brain Heart Int. Conf. Funct. Biomed. Imag., pp. 140–143, 2007,
[28] J. H. Moltz, L. Bornemann, J. M. Kuhnigk, V. Dicken, E. Peitgen, S. Meier, H. Bolte, M. Fabel, H. C. Bauknecht, M. Hittinger, A. Kießling, M. Püsken and H. O. Peitgen, “Advanced segmentation techniques for lung nodules, livermetastases, and enlarged lymph nodes in CT scans,” IEEE J. Sel. Topics Signal Process, vol. 3, no. 1, pp. 122-134, Feb. 2009.
[29] Z. Ma, J. M. Tavares, R. N. Jorge and T. Mascarenhas, “A review of algorithms for medical image segmentation and their applications to the female pelvic cavity,” Comput.Methods Biomechan. Biomed. Eng., vol. 13, no. 2, pp. 235-246, 2010.
[30] D. D. Brennan, P. F. Whelan, K. Robinson, O. Ghita, J. M. O'Brien, R. Sadleir and S. J. Eustace, “Rapid automated measurement of body fat distribution from whole-body MRI,” AJR Am J Roentgenol, vol. 185, pp. 418-23, 2005.
[31] J. Kullberg, H. Ahlstrom, L. Johansson and H. Frimmel, “Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI,” Int J Obes (Lond)., vol. 31, pp. 1806-1817, 2007.
[32] V. Positano, K. Cusi, M. F. Santarelli, A. Sironi, R. Petz, R. Defronzo, L. Landini and A. Gastaldelli, “Automatic correction of intensity inhomogeneities improves unsupervised assessment of abdominal fat by MRI,” J Magn Reson Imaging, vol. 28, pp. 403-410, 2008.
[33] V. Positano, A. Gastaldelli, A. M. Sironi, M. F. Santarelli, M. Lombardi and L. Landini, “An accurate and robust method for unsupervised assessment of abdominal fat by MRI,” J Magn Reson Imaging, vol. 20, pp. 684-689, 2004.
[34] D. L. Sussman, Y. Jianhua and R. M. Summers, “Automated measurement and segmentation of abdominal adipose tissue in MRI,” Biomedical Imaging, pp.936-939, 2010.
[35] J. C. Bezdek, R. Ehrlich and W. Full, “FCM: The Fuzzy C-Means clustering algorithm,” Computers & Geosciences, vol. 10, no. 2-3, pp. 191-203, 1984.
[36] J. Dehmeshki, X. Ye, H. Amin, M. Abaei, X. Lin and S. D. Qanadli, “Volumetric, quantification of atherosclerotic plaque in CT considering partial volume effect,” IEEE Transactions on Medical Imaging, vol. 26, no. 3, pp. 273-282, 2007.
[37] J. Dehmeshki, “An adaptive segmentation and 3-D visualization of the lungs,” Pattern Recognition Letters, vol. 20, pp. 919-926, 1999.
[38] S. Z. Li, “Random Field Modeling in Computer Vision,” New York: Springer-Verlag, pp. 1431-1488, 1995.
[39] F. W. Prior, K. Clark, P. Commean, J. Freymann, C. Jaffe, J. Kirby, S. Moore, K. Smith, L. Tarbox, B. Vendt and G. Marquez, “TCIA: An information resource to enable open science,” Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1282-1285, 2013.