Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images

Hanzheng Wang, Randy H. Moss, Xiaohe Chen, R. Joe Stanley, William V. Stoecker, M. Emre Celebi, Joseph M. Malters, James M. Grichnik, Ashfaq A. Marghoob, Harold S. Rabinovitz, Scott W. Menzies, Thomas M. Szalapski

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


In previous research, a watershed-based algorithm was shown to be useful for automatic lesion segmentation in dermoscopy images, and was tested on a set of 100 benign and malignant melanoma images with the average of three sets of dermatologist-drawn borders used as the ground truth, resulting in an overall error of 15.98%. In this study, to reduce the border detection errors, a neural network classifier was utilized to improve the first-pass watershed segmentation; a novel "edge object value (EOV) threshold" method was used to remove large light blobs near the lesion boundary; and a noise removal procedure was applied to reduce the peninsula-shaped false-positive areas. As a result, an overall error of 11.09% was achieved.

Original languageEnglish (US)
Pages (from-to)116-120
Number of pages5
JournalComputerized Medical Imaging and Graphics
Issue number2
StatePublished - Mar 2011
Externally publishedYes


  • Image processing
  • Malignant melanoma
  • Neural network
  • Segmentation
  • Watershed

ASJC Scopus subject areas

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


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