Modeling the dermoscopic structure pigment network using a clinically inspired feature set

Abstract

We present a method to detect and classify the dermoscopic structure pigment network which may indicate early melanoma in skin lesions. We locate the network as darker areas constituting a mesh, as well as lighter areas representing the 'holes' which the mesh surrounds. After identifying the lines and holes, 69 features inspired by the clinical definition are derived and used to classify the network into one of two classes: Typical or Atypical. We validate our method over a large, inclusive, 'real-world' dataset consisting of 436 images and achieve an accuracy of 82% discriminating between three classes (Absent, Typical or Atypical) and an accuracy of 93% discriminating between two classes (Absent or Present). © 2010 Springer-Verlag.