利用混合模型的方法對正子電腦斷層掃描影像做影像分割並與K-means及常態混合模型的方法比較

Segmentation of PET/CT images by Flexible Mixture Models and Comparison with K-means and Gaussian Mixture Models

葉孟樵(Meng-Ciao Ye)

指導教授 ： 盧鴻興

國立交通大學/理學院/統計學研究所/碩士(2009年)

https://doi.org/10.6842/NCTU.2009.00292

摘要

正子電腦斷層掃描影像能協助醫師判定異常部位，在PET影像上特異的亮點或暗點則表示這些異常部位可能發生的位置，因此PET影像的分割是非常重要。我們使用了以下方法去進行影像的分割以圈選出我們所感興趣的區域，包含了K-means, Gaussian mixture model (GMM)以及Flexible mixture model (FMM)。FMM與GMM最大的差異在於兩者所使用的混合分配，FMM此混合模型多考慮了PET影像的結構的特性，使用右偏分配估計背景的部份，另外以常態分配的混合估計非背影之影像。而FMM之分群結果也比K-means與GMM較佳。

關鍵字

k平均演算法 ； 高斯混合模型 ； 混合模型 ； 核密度估計

並列摘要

Positron Emission Tomography (PET) helps doctors determine the abnormal regions. The specific brightened regions in PET images show the location of abnormal region. Hence the segmentation of the data form PET images is very important. There are three methods to classify the data from PET image to obtain the region of interest, K-means with KDE, Gaussian mixture model (GMM) with KDE and flexible mixture model (FMM) with KDE. The main difference between GMM and FMM is that, GMM uses several normal distributions to fit the original PET data, while FMM does not. FMM considers the property and structure of PET image data. It uses a right-skewed distribution to fit the background images. The mixture normal distribution is used to fit other regions. Finally, the result of FMM with KDE is better than the result of K-means with KDE and GMM with KDE.

並列關鍵字

K-means ； Gaussian mixture model ； Flexible mixture model ； Kernel Density Estimation

參考文獻

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