Skin lesion image segmentation using Delaunay Triangulation for melanoma detection

Andrea Pennisi, Domenico D. Bloisi, Daniele Nardi, Anna Rita Giampetruzzi, Chiara Mondino, Antonio Facchiano

Research output: Contribution to journalArticlepeer-review


Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.

Original languageEnglish
JournalComputerized Medical Imaging and Graphics
Publication statusAccepted/In press - Jul 3 2015


  • Automatic segmentation
  • Border detection
  • Dermoscopy images
  • Melanoma detection

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition


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