Image segmentation metric and its application in the analysis of microscopic image

Abstract

Material microstructure data are an important type of data in building intrinsic relationships between compositions, structures, processes, and properties, which are fundamental to material design. Therefore, the quantitative analysis of microstructures is essential for effective control of the material properties and performances of metals or alloys in various industrial applications. Microscopic images are often used to understand the important structures of a material, which are related to certain properties of interest. One of the key steps during material design process is the extraction of useful information from images through microscopic image processing using computational algorithms and tools. For example, image segmentation, which is a task that divides the image into several specific and unique regions, can detect and separate each microstructure to quantitatively analyze its size and shape distribution. This technique is commonly used in extracting significant information from microscopic images in material structure characterization field. With great improvement in computing power and methods, a large number of image segmentation methods based on different theories have made great progress, especially deep learning-based image segmentation method. Therefore selecting an appropriate evaluation method to assess the accuracy and applicability of segmentation results to properly select the optimal segmentation methods and their indications on the direction of future improvement is necessary. In this work, 14 evaluation metrics of image segmentation were summarized and discussed. The metrics were divided into five categories: pixel, intra class coincidence, edge, clustering, and instance based. In the application of material microscopic image analysis, we collected two classical datasets (Al–La alloy and polycrystalline images) to conduct quantitative experiment. The performance of different segmentation methods and different typical noises in different evaluation metrics were then compared and discussed. Finally, we discussed the advantages and applicability of various evaluation metrics in the field of microscopic image processing.

Keywords

• computer vision
• image segmentation
• image processing
• segmentation evaluation metrics
• material microscopic image