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標題: 類神經網路與隱藏式馬可夫隨機領域模型於腦部核磁共振影像分割之研究
On the Segmentation of Brain MR Images using Neural Network and Hidden Markov Random Field Model
作者: 宋威廷
Sung, Wei-Ting
關鍵字: MR images;核磁共振造影;Neural Network;hidden Markov random field model;類神經網路;隱藏式馬可夫隨機領域模型
出版社: 通訊工程研究所

The segmentation of the various brain tissues is very valuable in the analysis of brain MR images, and it has a wide range of applications such as clinical analysis and visual inspection. In this thesis, we first use a pre-processing technique, called skull-stripping, and then we acquire an initial segmentation of a brain MR image through a neural network. Since the neural network does not consider the information of the neighborhood, the neural network can only work well on images with low levels of noise. So we use the hidden Markov random field to classify brain MR images after the initial segmentation of the neural network. Finally, we associate with three different segmentation results of T1-weighted, T2-weighted and PD-weighted to obtain the final segmentation.
Appears in Collections:通訊工程研究所

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