title: Skin pattern analysis for lesion classification using local isotropy.
abstract: OBJECTIVE
  The observation that skin pattern tends to be disrupted by malignant skin lesions, but not by benign ones suggests that measurements of skin pattern disruption on simply captured white light optical clinical (WLC) skin images could be a useful contribution to a diagnostic feature set. Previous work, which generated a flow field of skin pattern using a measurement of local line direction and intensity, was encouraging. The aim of this paper is to investigate the possibility of extracting new features using local isotropy metrics to quantify the skin pattern disruption.
  
  
  METHODS
  The skin pattern was extracted from WLC skin images by high-pass filtering. A local tensor matrix was computed. The local isotropy was measured by the condition number of the local tensor matrix. The difference of this measure over the lesion and normal skin areas, combined with the local line direction and the ABCD features, was used as a lesion classifier.
  
  
  RESULTS
  A set of images of malignant melanoma and benign naevi was analysed. A one-dimensional scatter plot showed the potential of a local isotropy metric, showing an area of 0.70 under the receiver operating characteristic (ROC) curve. A two-dimensional scatter plot, combined with the local line direction, indicated enhancement of the classification performance, showing an area of 0.89 under the ROC curve. A three-dimensional scatter plot combined with the local line direction and the ABCD features, using principal component analysis, demonstrated excellent separation of benign and malignant lesions. An ROC plot for this case enclosed an area of 0.96.
  
  
  CONCLUSIONS
  The experimental results show that the local isotropy metric has a potential to increase lesion classifier accuracy. Combined with the local line direction and the ABCD features, it is very promising as a method to distinguish malignant melanoma from benign lesions.
authors.affiliation: Institute of Arts, Science & Technology, Glyndwr University, University of Wales, Wrexham, UK. z.she@glyndwr.ac.uk
authors.forename: Zhishun
authors.initials: Z
authors.lastname: She
authors.forename: Peter S
authors.initials: PS
authors.lastname: Excell
mesh_terms: D046169:Dermoscopy / Q000295:instrumentation / Q000379:methods*; D003937:Diagnosis, Differential; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D008545:Melanoma / Q000145:classification / Q000473:pathology*; D008962:Models, Theoretical; D009369:Neoplasms / Q000145:classification / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D020795:Photophobia; D012867:Skin / Q000473:pathology; D012878:Skin Neoplasms / Q000145:classification / Q000473:pathology*