A novel method for detection of pigment network in dermoscopic images using graphs

We describe a novel approach to detect and visualize pigment network structures in dermoscopic images, based on the fact that the edges of pigment network structures form cyclic graphs which can be automatically detected and analyzed. First we perform a pre-processing step of image enhancement and edge detection. The resulting binary edge image is converted to a graph and the defined feature patterns are extracted by finding cyclic subgraphs corresponding to skin texture structures. We filtered these cyclic subgraphs to remove other round structures such as globules, dots, and oil bubbles, based on their size and color. Another high-level graph is created from each correctly extracted subgraph, with a node corresponding to a hole in the pigment network. Nodes are connected by edges according to their distances. Finally the image is classified according to the density ratio of the graph. Our results over a set of 500 images from a well known atlas of dermoscopy show an accuracy of 94.3% on classification of the images as pigment network Present or Absent.