不管是代謝綜合症及冠狀動脈粥樣硬化，這些病徵被許多論文證實與內臟脂肪體積有因果關係，心外膜脂肪的厚度被列為診斷心血管疾病的潛在因子。在許多臨床研究中，影像處理技術經常被應用在3-D醫學影像上。傳統的閥值二分法在計算醫學影像的脂肪體積時，經常因為醫學掃瞄儀器的處理造成拍攝物體與實際物體的誤差；此影響稱為部分體積效應。為了改善這個問題，我們提出自動化量測心外膜脂肪體積的演算法；此模型應用主動輪廓演算法用於影像的分割，同時提出部分體積校正法量測心臟脂肪體積，此法結合脂肪組織中像素強度與組織結構的特性作為權重因子，其量測脂肪組織的結果為脂防所佔的比例來表示，並使脂肪量測準確性提升。

A large volume of research has confirmed an association between comprehensive metabolic syndrome and coronary atherosclerosis, both of which show excess visceral fat volume. The thickness of epicardial fat is an important factor in the diagnosis of these disorders. Many papers have proposed that epicardial fat is associated with cardiovascular disease. However, in many clinical studies, image-processing technology often used to create 3-D medical images. This often depends on calculation of medical images by traditional dichotomy threshold volume algorithms. Generally, errors in medical instrument scan processing occur by the shooting object and the actual object. In order to resolve this problem, we proposed automated measurement of epicardial fat volume algorithms. In this model, active contour algorithm has been used for image segmentation. We proposed partial volume correction method to measure the epicardial adipose volume. This method combines pixel intensity and structure of adipose tissue with weight factors. Our results showed that accuracy in fat measurement improved significantly. In addition, the epicardial adipose tissue, represented by a part of adipose tissue, and fat measurement accuracy was significantly improved.

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