Nekovei R, Sun Y
Remote Sensing Lab., Rhode Island Univ., Narragansett, RI.
IEEE Trans Neural Netw. 1995;6(1):64-72. doi: 10.1109/72.363449.
A neural-network classifier for detecting vascular structures in angiograms was developed. The classifier consisted of a multilayer feedforward network window in which the center pixel was classified using gray-scale information within the window. The network was trained by using the backpropagation algorithm with the momentum term. Based on this image segmentation problem, the effect of changing network configuration on the classification performance was also characterized. Factors including topology, rate parameters, training sample set, and initial weights were systematically analyzed. The training set consisted of 75 selected points from a 256x256 digitized cineangiogram. While different network topologies showed no significant effect on performance, both the learning process and the classification performance were sensitive to the rate parameters. In a comparative study, the network demonstrated its superiority in classification performance. It was also shown that the trained neural-network classifier was equivalent to a generalized matched filter with a nonlinear decision tree.
开发了一种用于检测血管造影中血管结构的神经网络分类器。该分类器由一个多层前馈网络窗口组成，其中中心像素使用窗口内的灰度信息进行分类。该网络使用带有动量项的反向传播算法进行训练。基于此图像分割问题，还对改变网络配置对分类性能的影响进行了表征。系统分析了包括拓扑结构、速率参数、训练样本集和初始权重等因素。训练集由从256×256数字化电影血管造影中选取的75个点组成。虽然不同的网络拓扑结构对性能没有显著影响，但学习过程和分类性能对速率参数都很敏感。在一项对比研究中，该网络在分类性能上显示出其优越性。还表明，经过训练的神经网络分类器等同于具有非线性决策树的广义匹配滤波器。
