Medical image analysis of MRI brain images by deep RBF GMDH-type neural network using principal component-regression analysis,
Medical image analysis of MRI brain images by deep RBF GMDH-type neural network using principal component-regression analysis,
复制标题
使用主成分回归分析，通过深度 RBF GMDH 型神经网络对 MRI 脑图像进行医学图像分析，
DOI：
10.1109/iiai-aai.2015.249
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Junji Ueno and Shoichiro Takao
中科院分区：
文献类型：
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作者：
Tadashi Kondo;Junji Ueno and Shoichiro Takao
将深径向基函数(RBF)数据处理(GMDH)类神经网络应用于磁共振脑图像的医学图像分析。在这种深度RBF GMDH型神经网络算法中，利用启发式自组织方法自动生成和组织许多隐含层，以适应非线性系统的复杂性，这是GMDH算法的基本前提。这种启发式自组织方法是进化计算的一种。本文将深度RBF GMDH型神经网络应用于MRI脑图像的医学图像分析。利用深度RBF GMDH型神经网络准确地识别和提取了大脑区域，即大脑中的白质和灰质区域。将这些识别结果与传统的用反向传播方法训练的Sigmoid函数神经网络的识别结果进行了比较。
The deep radial basis function (RBF) Group Method of Data Handling (GMDH)-type neural network is applied to the medical image analysis of magnetic resonance imaging (MRI) brain images. In this deep RBF GMDH-type neural network algorithm, many hidden layers are automatically generated and organized so as to fit the complexity of the nonlinear systems by using the heuristic self-organization method which is the basic premise of the GMDH algorithm. This heuristic self-organization method is a type of evolutionary computation. In this study, the deep RBF GMDH-type neural network is applied to the medical image analysis of MRI brain image. The brain regions, the white and gray matter regions in the brain, are recognized and extracted accurately using the deep RBF GMDH-type neural network. These recognition results are compared with those obtained using the conventional sigmoid function neural network trained using the back propagation method.