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

Abstract

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.

Fig. 1

Relative rgb space showing one bin of dimension 4 × 4 × 4 and its three-color bin edges.

Fig. 2

Dermoscopy image examples of melanoma and benign lesions. (a) Melanoma image. (b) Benign image (dysplastic nevus).

Fig. 3

WDD functions used for computing correlation-based features (20). WDD, weighted density distribution.

Fig. 4

Boundary area percentage example using 25% of the lesion area for analysis (white region) [This is the same lesion as in Fig. 1(a)].

Fig. 5

Luminance histogram example for melanoma and benign mapped colors for a melanoma lesion for the 25% boundary area case. (a) Melanoma lesion image. (b) Luminance histogram for melanoma mapped colors using the statistical approach. (c) Luminance histogram for benign mapped colors using the fuzzy logic-based approach for α=0.1.

Fig. 6

Luminance histogram example for melanoma and benign mapped colors for a benign lesion for the 25% boundary area case. (a) Benign lesion image. (b) Luminance histogram for melanoma mapped colors using the statistical approach. (c) Luminance histogram for benign mapped colors using the fuzzy logic-based approach for α=0.1.