Fuzzy logic color detection: Blue areas in melanoma dermoscopy images

Mounika Lingala, R. Joe Stanley, Ryan K. Rader, Jason Hagerty, Harold S. Rabinovitz, Margaret Oliviero, Iqra Choudhry, William V. Stoecker

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Fuzzy logic image analysis techniques were used to analyze three shades of blue (lavender blue, light blue, and dark blue) in dermoscopic images for melanoma detection. A logistic regression model provided up to 82.7% accuracy for melanoma discrimination for 866 images. With a support vector machines (SVM) classifier, lower accuracy was obtained for individual shades (79.9-80.1%) compared with up to 81.4% accuracy with multiple shades. All fuzzy blue logic alpha cuts scored higher than the crisp case. Fuzzy logic techniques applied to multiple shades of blue can assist in melanoma detection. These vector-based fuzzy logic techniques can be extended to other image analysis problems involving multiple colors or color shades.

Original languageEnglish (US)
Pages (from-to)403-410
Number of pages8
JournalComputerized Medical Imaging and Graphics
Issue number5
StatePublished - Jul 2014
Externally publishedYes


  • Blue area
  • Dermoscopy
  • Dysplastic nevi
  • Fuzzy logic
  • Image analysis
  • Melanoma

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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