Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

Azmath Khan, Kapil Gupta, R. J. Stanley, William V. Stoecker, Randy H. Moss, Giuseppe Argenziano, H. Peter Soyer, Harold S. Rabinovitz, Armand B. Cognetta

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%.

Original languageEnglish (US)
Pages (from-to)50-57
Number of pages8
JournalComputerized Medical Imaging and Graphics
Issue number1
StatePublished - Jan 2009
Externally publishedYes


  • Asymmetric blotches
  • Dermoscopy
  • Fuzzy logic
  • Image analysis
  • Malignant melanoma
  • Neural network

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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