結合正子斷層掃描和電腦斷層掃描的PET/CT（Positron Emission Tomography/ Computer Tomography）問世之後，成功地將功能性攝影和人體解剖構造結合在一起，提供卓越的臨床診斷價值。然而PET受制於衰減效應（Attenuation）的影響，無法提供準確地定量分析結果。本研究的目的在於提出一套自動化的電腦斷層影像分割方法，來幫助正子斷層掃描的衰減修正（Attenuation Correction），以期增加衰減修正的準確性以及臨床上的實用性。本研究的方法以模糊平均數群聚演算法（Fuzzy C-Means Clustering, FCM）為架構，直接探討影像點在影像中的分布關係，整體地評估區域的分布特性，打破傳統的影像分割方法中，必須以影像值為測量空間的侷限。本研究所提出之複合式FCM演算法（Mixed Fuzzy K-Means Clustering, Mixed FCM），結合了兩種影像屬性：影像值的區域變化和影像值的大小，改善群聚分析中缺乏影像點的空間資訊。由實驗的結果可以顯示，利用複合式FCM演算法不但能更正確地區分出骨骼和空氣區域，凸顯骨骼和空氣在解剖空間上的位置，並且亦改善了顯影劑（Contrast Agent）對於衰減修正的影響，增加衰減修正的準確性。

A PET/CT (Positron Emission Tomography/ Computer Tomography) has unique capability of acquiring accurately aligned functional and anatomical images for human body, and supplies the excellent worth in clinical diagnosis. However, PET images are not able to provide correct quantitative analysis due to the attenuation of photons. Many researches have applied computed tomography (CT) data as X-ray based attenuation correction for positron emission tomography (PET) imaging. In this study, we present an automatic segmented method of CT images in whole body scan to improve attenuation correction in PET imaging. A mixed fuzzy C-means (FCM) clustering which combines the use of the intensity attribute of the homogeneous objects with the standard deviation attribute of the inhomogeneous objects is introduced. The experimental results indicate that this method not only enhances the anatomical localization of bone and air in CT images, but also reduces the influence from the contrast agent. Besides, it reduces the bias due to transmission data, and promotes the practical utility in clinical diagnosis.

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