Grain segmentation of sandstone images based on convolutional neural networks and weighted fuzzy clustering

Abstract

Grain segmentation of sandstone images is to partition the images into non-overlapping regions, each of which is an independent mineral grain. However, a sandstone image usually contains hundreds of mineral grains and complicated microstructures, which makes current segmentation methods inefficient. In this study, the authors propose a three-stage framework for the automatic segmentation of sandstone images. In the first stage, the input sandstone images are pre-segmented into over-segmented mineral superpixels. In the second stage, the instance-independent features are extracted by a specially designed convolutional neural network, and the instance-aware features are extracted by computing histogram statistics and Gabor responses of mineral superpixels. In the third stage, a novel weighted fuzzy clustering algorithm is proposed to cluster the mineral superpixels into different classes, afterwards the adjacent mineral superpixels are merged to yield the complete minerals according to their classes. The experimental results conducted on the sandstone image datasets demonstrate the effectiveness of the proposed method, which evidently outperform the state-of-the-art segmentation methods.