Skin lesion segmentation (SLS) has a vital role in the early and precise diagnosis of skin cancer by computer-aided diagnosis (CAD) systems. But, the automatic SLS in dermoscopic images is a challenging task due to the substantial differences in color, texture, artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast, and varying sizes, position, and shapes of the lesion images. In the paper, we propose an extended GrabCut image segmentation algorithm for Foreground/Backgrounds dermoscopic image segmentation applications. The method integrates octree color quantization and a modified GrabCut method with a new energy function. Extensive computer simulation on ISIC 2017 has shown to compare favorably on both qualitative and quantitative evaluations with commonly used segmentation tools.
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