Colour analysis of skin lesion regions for melanoma discrimination in clinical images

Abstract

Background: Skin lesion colour is an important feature for diagnosing malignant melanoma. Colour histogram analysis over a training set of images has been used to identify colours characteristic of melanoma, i.e., melanoma colours. A percent melanoma colour feature defined as the percentage of the lesion pixels that are melanoma colours has been used as a feature to discriminate melanomas from benign lesions.

Methods: In this research, the colour histogram analysis technique is extended to evaluate skin lesion discrimination based on colour feature calculations in different regions of the skin lesion. The colour features examined include percent melanoma colour and a novel colour clustering ratio. Experiments are performed using clinical images of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi.

Results: Experimental results show improved discrimination capability for feature calculations focused in the lesion boundary region. Specifically, correct melanoma and benign recognition rates are observed as high as 89 and 83%, respectively, for the percent melanoma colour feature computed using only the outermost, uniformly distributed 10% of the lesion's area.

Conclusions: The experimental results show for the features investigated that the region closest to the skin lesion boundary contains the greatest colour discrimination information for lesion screening. Furthermore, the percent melanoma colour feature consistently outperformed the colour clustering ratio for the different skin lesion regions examined. The clinical application of this result is that clustered colours appear to be no more significant than colours of arbitrary distribution within a lesion.

Fig. 1

Clinical image examples of benign and melanoma skin lesions, (a) Melanoma image (early invasive), (b) Benign image (seborrheic keratosis).

Fig. 2

Boundary area percentage example using 25% of the lesion area for analysis (black region). (This is the same lesion as in Fig. 1(b)).

Fig. 3

Offset boundary area example using 50% of the lesion area as offset (gray region) and 25% of the lesion area for analysis (black region). (This is the same lesion as in Fig. 1(b)).

Fig. 4

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

Fig. 5

Percent melanoma color feature training true positive and true negative rates over all thresholds D for one training/test set for the 10% boundary area percentage case. The arrow points to the threshold D where the true positive and true negative rates are equal.

Fig. 6

Corresponding percent melanoma color feature test true positive and true negative rates over all thresholds D. The dotted line points to the threshold D (determined from the training data in Fig. 5) for determining true positive and true negative test rates.

Fig. 7

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

Fig. 8

Offset boundary area percentage average and standard deviation test results over 18 randomly chosen training/test sets for the percent melanoma color and color clustering ratio features. The horizontal axis shows the percentage of the lesion area offset from the lesion boundary (% area offset) and the percentage of lesion area used for feature calculations starting from the inner boundary of the offset region (% area features).

Fig. 9

10% 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).