Entropy-assisted image segmentation for nano- and micro-sized networks

Image analysis is an important tool for characterizing nano/micro network structures. To understand the connection, organization and proper alignment of network structures, the knowledge of the segments that represent the materials inside the image is very necessary. Image segmentation is generally carried out using statistical methods. In this study, we developed a simple and reliable masking method that improves the performance of the indicator kriging method by using entropy. This method selectively chooses important pixels in an image (optical or electron microscopy image) depending on the degree of information required to assist the thresholding step. Reasonable threshold values can be obtained by selectively choosing important pixels in a complex network image composed of extremely large numbers of thin and narrow objects. Thus, the overall image segmentation can be improved as the number of disconnected objects in the network is minimized. Moreover, we also proposed a new method for analyzing high-pixel resolution images on a large scale and optimized the time-consuming steps such as covariance estimation of low-pixel resolution image, which is rescaled by performing the affine transformation on high-pixel resolution images. Herein, image segmentation is executed in the original high-pixel resolution image. This entropy-based masking method of low-pixel resolution significantly decreases the analysis time without sacrificing accuracy.