Auto-segmentation technique for SEM images using machine learning: Asphaltene deposition case study

Abstract

Enormous efforts have been attempted to replace the subjective human analysis of digital images by automated computational methods such as image processing and computer vision. The image processing methods (i.e., pixel- and object-based assessments) determine a variety of spectral/optical features and devices/data properties on the images so that appropriate information could be extracted. However, these methods may not be appropriate when globally heterogeneous and locally anisotropic features exist such as those found in Scanning Electron Microscopy (SEM) Images. Thus, it is essential to have an adaptive and data-driven procedure to extract optimal information from individual SEM images. In this study, we developed a fully automated image processing and analysis method using data analytics, pattern recognition, and machine learning (including deep learning) techniques to automate the image processing and investigate physical properties and nanoscale deposition of petroleum constituents such as asphaltenes on surfaces from over hundreds of images. To do so, various data preparation processes (i.e., image filtering and quality assessment methods) were first introduced to mine and enhance the data. Then, the extracted information was used to identify and quantify targeted physical properties and deposition attributes by denosing and image segmentation techniques. To validate the proposed method, we applied the model to the experimental results from asphaltene deposition studies. The model results were then compared with the corresponding experimental counterparts from the literature. The insight from this application led to a better understanding of the asphaltene deposition mechanism. To the best of our knowledge, this work is one of the first attempts to develop a fully automated image-processing model that describes physical properties of deposited species. In addition, our combination of data and descriptive models allowed us to differentiate foreground and background information particles from SEM images meaning that the model could be used for other applications in image processing.