A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images

Abstract

Background: Skin lesion color is an important feature for diagnosing malignant melanoma. In previous research, skin lesion color was investigated for discriminating malignant melanoma lesions from benign lesions in clinical images. Colors characteristics of melanoma were determined using color histogram analysis over a training set of images. Percent melanoma color and color clustering ratio features were used to quantify the presence of melanoma-colored pixels within skin lesions for skin lesion discrimination.

Methods: In this research, the relative color histogram analysis technique is used to evaluate skin lesion discrimination based on color feature calculations in different regions of the skin lesion in dermoscopy images. The histogram analysis technique is examined for varying training set sizes from the set of 113 malignant melanomas and 113 benign dysplastic nevi images.

Results: Experimental results show improved discrimination capability for feature calculations focused in the interior lesion region. Recognition rates for malignant melanoma and dysplastic nevi as high as 87.7% and 74.9%, respectively, are observed for the color clustering ratio computed using the outer 75% uniformly distributed area with a 10% offset within the boundary.

Conclusions: Experimental results appear to indicate that the melanoma color feature information is located in the interior of the lesion, excluding the 10% central-most region. The techniques presented here including the use of relative color and the determination of benign and malignant regions of the relative color histogram may be applicable to any set of images of benign and malignant lesions.

Fig. 1

Overview of algorithm for color feature calculations and lesion discrimination.

Fig. 2

Dermoscopy image examples of melanoma and benign lesions: (a) melanoma image, (b) benign image (dysplastic nevus).

Fig. 3

Example of three-dimensional relative color histogram bin labeling for a training set of images. The melanoma-labeled bins are red regions. The benign-labeled bins are green regions.

Fig. 4

Boundary area percentage example using 25% of the lesion area for analysis (white region). (This is the same lesion as in Fig. 2(a).)

Fig. 5

Offset boundary area example using 10% of the lesion area as offset (gray region) and 75% of the lesion area for analysis (white region). (This is the same lesion as in Fig. 1(a).)

Fig. 6

Average and standard deviation melanoma test results over 18 test sets for the boundary area percentage cases of 100%, 90%, 75%, 50%, 25%, and 10%. PMC and CCR refer to the percent melanoma color and color clustering ratio features, respectively.

Fig. 7

Ten percent offset boundary area percentage average and standard deviation test results for 90%, 75%, 50%, 25%, and 10% lesion area cases starting from the inner boundary of the offset region over 18 test sets. The horizontal axis shows the percentage of the lesion area used for feature calculations (% area features).

Fig. 8

Hundred percent boundary area percentage case average and standard deviation test results over 18 test sets for percentage of melanoma color feature using histogram bin thresholds K=0.00125A_L and K=0.

Fig. 9

Average and standard deviation melanoma test results over 18 test sets for the 100% boundary area percentage case using 25%, 50%, 75%, and 100% of the training images.