Automatic segmentation and melanoma detection based on color and texture features in dermoscopic images

Abstract

Purpose: Melanoma is known as the most aggressive form of skin cancer and one of the fastest growing malignant tumors worldwide. Several computer-aided diagnosis systems for melanoma have been proposed, still, the algorithms encounter difficulties in the early stage of lesions. This paper aims to discriminate melanoma and benign skin lesion in dermoscopic images.

Methods: The proposed algorithm is based on the color and texture of skin lesions by introducing a novel feature extraction technique. The algorithm uses an automatic segmentation based on k-means generating a fairly accurate mask for each lesion. The feature extraction consists of the existing and novel color and texture attributes measuring how color and texture vary inside the lesion. To find the optimal results, all the attributes are extracted from lesions in five different color spaces (RGB, HSV, Lab, XYZ, and YCbCr) and used as the inputs for three classifiers (K nearest neighbors, support vector machine , and artificial neural network).

Results: The PH2 set is used to assess the performance of the proposed algorithm. The results of our algorithm are compared to the results of published articles that used the same dataset, and it shows that the proposed method outperforms the state of the art by attaining a sensitivity of 99.25%, specificity of 99.58%, and accuracy of 99.51%.

Conclusion: The final results show that the colors combined with texture are powerful and relevant attributes for melanoma detection and show improvement over the state of the art.

Keywords: K-means; classification; dermoscopy; features extraction; melanoma; segmentation; super-pixel.

FIGURE 1

Lesion segmentation (A): original image (B) k‐means segmentation

FIGURE 2

Corner segmentation by active contours (A) contour initialization (B) detected corners (C) overlay contour on the original image (d) lesion mask after removing corners

FIGURE 3

Filtering (A) lesion mask with extra objects (B) erosion (C) dilatation, and final mask

FIGURE 4

Lesions segmentation: Overlay automatic and manual contours

FIGURE 5

Lesion pixilated with a super‐pixel algorithm

FIGURE 6

K‐means segmentation. (A) A lesion with three classes. (B) A lesion with four classes