Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences
Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences
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具有输入特征选择的神经滤波器及其在医学图像序列图像质量改进中的应用
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发表时间：
2002
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通讯作者：
Ken
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作者：
Ken
为了进一步提高传统神经网络过滤器的性能，提出了一种新的神经网络过滤器，称为带特征神经网络(NFF)。为了解决输入特征的最优选择问题，我们提出了一种框架，该框架包括：1)人工选择与给定任务相关的输入特征的候选者；2)自动选择完成给定任务所需的最佳输入特征。实验结果表明，该框架在改善医用X射线图像序列的图像质量方面的性能优于传统的神经网络和动态滤波器。
SUMMARY In this paper, we propose a new neural filter to which the features related to a given task are input, called a neural filter with features (NFF), to improve further the performance of the conventional neural filter.In order to handle the issue concerning the optimal selection of input features, we propose a framework composed of 1) manual selection of candidates for input features related to a given task and 2) training with automatically selection of the optimal input features required for achieving the given task.Experiments on the proposed framework with an application to improving the image quality of medical X-ray image sequences were performed.The experimental results demonstrated that the performance on edge-preserving smoothing of the NFF, obtained by the proposed framework, is superior to that of the conventional neural and dynamic filters.