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dc.contributor.author葉孟樵en_US
dc.contributor.authorYe, Meng-Ciaoen_US
dc.contributor.author盧鴻興en_US
dc.contributor.authorLu, Horng-Shingen_US
dc.date.accessioned2014-12-12T01:30:55Z-
dc.date.available2014-12-12T01:30:55Z-
dc.date.issued2008en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT079626516en_US
dc.identifier.urihttp://hdl.handle.net/11536/42676-
dc.description.abstract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。zh_TW
dc.description.abstractPositron 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.en_US
dc.language.isoen_USen_US
dc.subjectk平均演算法zh_TW
dc.subject高斯混合模型zh_TW
dc.subject混合模型zh_TW
dc.subject核密度估計zh_TW
dc.subjectK-meansen_US
dc.subjectGaussian mixture modelen_US
dc.subjectFlexible mixture modelen_US
dc.subjectKernel Density Estimationen_US
dc.title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zh_TW
dc.titleSegmentation of PET/CT images by Flexible Mixture Models and Comparison with K-means and Gaussian Mixture Modelsen_US
dc.typeThesisen_US
dc.contributor.department統計學研究所zh_TW
Appears in Collections:Thesis


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