Fast-FineCut: Grain boundary detection in microscopic images considering 3D information

Abstract

The inner structure of a material is called its microstructure. It stores the genesis of a material and determines all the physical and chemical properties. However, the microstructure is highly complex and numerous image defects such as vague or missing boundaries formed during sample preparation, which makes it difficult to extract the grain boundaries precisely. In this work, we address the task of grain boundary detection in microscopic image processing and develop a graph-cut based method called Fast-FineCut to solve the problem. Our algorithm makes two key contributions: (1) An improved approach that incorporates 3D information between slices as domain knowledge, which can detect the boundaries precisely, even for the vague and missing boundaries. (2) A local processing method based on overlap-tile strategy, which can not only solve the "chain scission" problem at the edge of images, but also economize on the consumption of computing resources. We conduct experiments on a stack of 296 slices of microscopic images of polycrystalline iron (1600 × 2800) and compare the performance against several state-of-the-art boundary detection methods. We conclude that Fast-FineCut can detect boundaries effectively and efficiently.

Image Boundary, Corner, and Edge Detection: Past, Present, and Future

“…In the image boundary detection application [88], grain boundary extraction is an active research area, in the challenge faced a the researcher is that the microstructure of the grain is highly complex and various image defects and missing boundaries are formed throughout the sample preparation. These difficulties are causing the difficulty in extracting the grain boundary detection correctly.…”

WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network

“…[45] proposed the concept "propagation segmentation" based on graph-cut, it sets the energy function of the target image using the information of the last slice through the domain knowledge of material science. [32] improved it by changing the setting of binary terms in energy function, filling the blurred or missing boundary in target images with the same boundary in the last slice. The tracking-based method shows superior performance when dealing with blurred or missing boundary and spurious scratches.…”

“…In order to prove it, we have designed three sets of comparative experiments in the experimental stage, using mask, maskexpansion and weight map, respectively. The mask-dilation means a boundary dilation map on a mask, which comes from the concept of "bounding region" in [32], as shown in Figure 5. Secondly, we present a multi-level fusion strategy to make better use of multiple levels of information.…”

“…The mask contains strong edge information, which is harmful when integrated into the high-level features of the network. The mask-expansion integrates the concept of "bounding region" in [32], which is mainly used to characterize structural transitions between adjacent slices. Therefore, it works better when integrated into high-level features.…”

“…For 2D image analysis, flaws in material microscopic images seriously hinder the target object detection [32]. The region of interest in polycrystalline microscopic images is the single-pixel closed boundary of grain (like a cell in biological image) [6], as shown with black straight and thick arrows.…”