A novel approach for fracture skeleton extraction from rock surface images

Abstract The complexity and diversity of information on rock mass surfaces leads to disparities in many image properties among different rock mass surface images, including image brightness , color, contrast and pixel distribution. As a result, there is currently no unified automatic approach for extracting the rock fracture skeleton from the images. In this paper, according to common features of rock surface images and curvilinearity of fractures which are set as the extraction targets, a dark region curvilinear structure enhancement (DRCSE) algorithm for automatic rock fracture skeleton extraction is proposed. This algorithm mainly relies on the curvilinear structure enhancement filter Frangi in image pre-processing stage. Through multiple image processing, dark region contrast of rock surface images is enhanced while noise is reduced. After this process, the effect of Frangi filtering can be significantly improved. Particularly, by using Homomorphic filtering coupled Gamma transforming (HCG) to enhance dark region contrast, a new edge-entropy (EE) index is proposed to describe the contrast-enhanced image quality. Particle swarm optimization (PSO) is utilized to calculate the value of Gamma transforming parameters that maximize the EE index. The test results show that it has great advantages compared to traditional methods for rock fracture skeleton extraction.