Medical image recognition of lungs regions for multi-slice CT images by using the revised GMDH-type neural networks.

在这项研究中，我们开发了修订的GMDH（组数据处理方法）型神经网络算法，可以自动组织最佳的神经网络架构，适合肺部的X射线CT图像的特点，并将此开发的算法应用于肺部的医学图像识别。在这些算法中，最佳的神经网络结构是自动选择三种类型的神经网络结构，如S形函数型神经网络，径向基函数（RBF）型神经网络和多项式型神经网络。此外，结构参数，如层数，隐藏层中的神经元的数量和最佳的神经元结构被自动选择，以便最小化被定义为AIC（赤池信息准则）或PSS（预测平方和）的预测误差标准。这些算法还具有从许多图像特征中自选择最优输入变量的能力，以使预测误差准则AIC或PSS最小。因此，我们可以很容易地将这些修正的GMDH型神经网络算法用于医学图像识别，因为最佳的神经网络结构适合医学图像的特征是自动组织的。利用计算机中的自组织神经网络，对X线CT图像的肺部区域进行了自动提取，取得了较好的精度。

In this study, we developed the revised GMDH (Group Method of Data Handling)-type neural network algorithms which can automatically organize the optimum neural network architectures fitting the characteristics of X-ray CT images of the lungs and applied this developed algorism to medical image recognition of the lungs. In these algorithms, the optimum neural network architecture is automatically selected from three types of neural network architectures such as sigmoid function type neural networks, RBF (Radial Basis Function) type neural networks and polynomial type neural networks. Furthermore, the structural parameters such as the number of layers, the number of neurons in the hidden layers and optimum neuron architectures are automatically selected so as to minimize the prediction error criterion defined as AIC ( Akaike's Information Criterion) or PSS (Prediction Sum of Squares). These algorithms have another ability of self-selecting the optimum input variables from many image characteristics so as to minimize the prediction error criterion AIC or PSS. Therefore, we can easily these revised GMDH-type neural network algorithms to the medical image recognition because the optimum neural network architecture fitting the medical image characteristics is automatically organized.In this study, we are applying these revised GMDH-type neural network algorithms to the medical image recognition of the lungs and we organize the optimum neural network architectures fitting the image characteristics of the lungs. By using the organized neural networks in the computer, the regions of the lungs of the X-ray CT images are automatically extracted with the good accuracy.