A basis function feature-based approach for skin lesion discrimination in dermatology dermoscopy images

  1. R. Joe Stanley1,
  2. William V. Stoecker2,
  3. Randy H. Moss1,
  4. Harold S. Rabinovitz3,
  5. Armand B. Cognetta Jr4,
  6. Giuseppe Argenziano5,
  7. H. Peter Soyer6

Article first published online: 19 MAR 2008

DOI: 10.1111/j.1600-0846.2008.00307.x

Issue

Skin Research and Technology

Skin Research and Technology

Volume 14, Issue 4, pages 425–435, November 2008

Additional Information

How to Cite

Joe Stanley, R., Stoecker, W. V., Moss, R. H., Rabinovitz, H. S., Cognetta, A. B., Argenziano, G. and Peter Soyer, H. (2008), A basis function feature-based approach for skin lesion discrimination in dermatology dermoscopy images. Skin Research and Technology, 14: 425–435. doi: 10.1111/j.1600-0846.2008.00307.x

Author Information

  1. 1

    Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA,

  2. 2

    The Dermatology Center, Rolla, MO, USA,

  3. 3

    Skin Cancer and Associates, Plantation, FL, USA,

  4. 4

    Dermatology Associates of Tallahassee, Tallahassee, FL, USA,

  5. 5

    Department of Dermatology, Second University of Naples, Naples, Italy

  6. 6

    Department of Dermatology, University of Graz, Austria

*Address: R. Joe Stanley Department of Electrical and Computer Engineering Missouri University of Science and Technology 127 Emerson Electric Co. Hall 301 West 16th Street Rolla, MO 65409-0040 USA Tel: +1 573 341 6896 Fax: +1 573 341 4532 e-mail:stanleyj@mst.edu

Publication History

  1. Issue published online: 10 OCT 2008
  2. Article first published online: 19 MAR 2008
  3. Accepted for publication 29 December 2007

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Keywords:

  • image processing;
  • color;
  • malignant melanoma;
  • histogram;
  • fuzzy logic;
  • basis function

Background: Skin lesion color is an important feature for diagnosing malignant melanoma. New basis function correlation features are proposed for discriminating malignant melanoma lesions from benign lesions in dermoscopy images. The proposed features are computed based on correlating the luminance histogram of melanoma or benign labeled relative colors from a specified portion of the skin lesion with a set of basis functions. These features extend previously developed statistical and fuzzy logic-based relative color histogram analysis techniques for automated mapping of colors representative of melanoma and benign skin lesions from a training set of lesion images.

Methods: Using the statistical and fuzzy logic-based approaches for relative color mapping, melanoma and benign color features are computed over skin lesion region of interest, respectively. Luminance histograms are obtained from the melanoma and benign mapped colors within the lesion region of interest and are correlated with a set of basis functions to quantify the distribution of colors. The histogram analysis techniques and feature calculations are evaluated using a data set of 279 malignant melanomas and 442 benign dysplastic nevi images.

Results: Experimental test results showed that combining existing melanoma and benign color features with the proposed basis function features found from the melanoma mapped colors yielded average correct melanoma and benign lesion discrimination rates as high as 86.45% and 83.35%, respectively.

Conclusions: The basis function features provide an alternative approach to melanoma discrimination that quantifies the variation and distribution of colors characteristic of melanoma and benign skin lesions.

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