Segmentation of petrographic images by integrating edge detection and region growing ?
A novel approach to segmenting petrographic images is proposed in this paper. A series of edge operators with various sizes of masks are first defined. By considering a larger neighborhood, the effects of noise or surface irregularities on edges are reduced. Color edges in an image are obtained by combining the edge operators and a color edge detection algorithm. A seeded region-growing algorithm is then used to segment the image based on the color edge information and the distances between edge-pixels and non-edge pixels. Seed regions are created automatically. These regions grow simultaneously. After all pixels in the image are labeled, the boundaries shared by two regions are checked. If a boundary is weak enough, it is eliminated and the corresponding two regions are merged. In the ultimate segmented map, each region whose size is large enough corresponds to a mineral grain in the image. This approach has been implemented in C++ under the Linux environment. Three sets of petrographic images were used to test the method.
edge operator seed region mineral grain grain boundary detection region labeling