We present new algorithms for segmenting neuron images which are taken from cells being grown in culture with oxidative agents. Information from changing images can be used to compare changes in neurons from the Zellweger mice to those from normal mice. Image segmentation is the first and major step for the study of these different types of processes in neuron cells. It is difficult to do it as these neuron cell images from stained fields and unimodal histograms. In this paper we develop an innovative strategy for the segmentation of neuronal cell images which are subjected to stains and whose histograms are unimodal. The proposed method is based on logical analysis of grey difference. Two key parameters, window width and logical threshold, are automatically extracted to be used in logical thresholding method. Spurious regions are detected and removed by using hierarchical filtering window. Experiment and comparison results show the efficient of our algorithms.
机译:是什么引起了背散射电子图像中C-S-H凝胶灰度的差异?
机译:使用灰度和形状几何描述符对条带声纳背向散射图像中的感兴趣区域进行量化:TargAn软件
机译:使用灰度和形状几何描述符对条带声纳背向散射图像中的感兴趣区域进行量化:TargAn软件
机译:使用灰度差异对神经图像进行量化
机译:图论方法用于量化神经成像中的灰色物质体积。
机译:用于量化和分析实验获得的图像形态差异的多功能方法
机译:使用灰度差异对神经图像进行量化