title: Novel Method for Border Irregularity Assessment in Dermoscopic Color Images.
abstract: BACKGROUND
  One of the most important lesion features predicting malignancy is border irregularity. Accurate assessment of irregular borders is clinically important due to significantly different occurrence in benign and malignant skin lesions.
  
  
  METHODS
  In this research, we present a new approach for the detection of border irregularities, as one of the major parameters in a widely used diagnostic algorithm the ABCD rule of dermoscopy. The proposed work is focused on designing an efficient automatic algorithm containing the following steps: image enhancement, lesion segmentation, borderline calculation, and irregularities detection. The challenge lies in determining the exact borderline. For solving this problem we have implemented a new method based on lesion rotation and borderline division.
  
  
  RESULTS
  The algorithm has been tested on 350 dermoscopic images and achieved accuracy of 92% indicating that the proposed computational approach captured most of the irregularities and provides reliable information for effective skin mole examination. Compared to the state of the art, we obtained improved classification results.
  
  
  CONCLUSIONS
  The current study suggests that computer-aided system is a practical tool for dermoscopic image assessment and could be recommended for both research and clinical applications. The proposed algorithm can be applied in different fields of medical image analysis including, for example, CT and MRI images.
authors.affiliation: Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Aleja Mickiewicza 30, 30-059 Krakow, Poland.
authors.forename: Joanna
authors.initials: J
authors.lastname: Jaworek-Korjakowska
mesh_terms: D000465:Algorithms; D003116:Color; D046169:Dermoscopy / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008545:Melanoma / Q000175:diagnosis* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000473:pathology