Rock grains segmentation using curvilinear structures based features

The segmentation method for rock grains delineation on rock material images is presented. The method is based on a five-dimensional intensity feature vector calculated for each point of the analyzed image. The features include pixel's grey level, grey level average and standard deviation calculated for the pixel's neighbourhood as well as vesselness and vesselness scale parameters. The vesselness and vesselness scale features measure local curvilinearity of objects depicted in the image. Machine learning classifiers, such as k nearest neighbours, support vector machine and artificial neural network are used for edges of rocks' grains determination. The manually segmented images were used as a ground truth. The bunch of experiments were performed to discover the best features calculation and classification methods parameters. The post-processing methods (thinning and best-fit) were proposed to improve the delineation process. The obtained results show that accuracy as high as 89% can be expected.