Automated skin lesion segmentation via image-wise supervised learning and multi-scale superpixel based cellular automata

Abstract

Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for automated melanoma diagnosis. Existing methods however have problems with over-or under-segmentation and do not perform well when a lesion is partially connected to the background or when the image contrast is low. To overcome these limitations, we propose a new automated skin lesion segmentation method via image-wise supervised learning (ISL) and multi-scale superpixel based cellular automata (MSCA). We propose using ISL to derive a probabilistic map for automated seeds selection, which removes the reliance on user-defined seeds as in conventional methods. The probabilistic map is then further used with the MSCA model for skin lesion segmentation. This map enables the inclusion of additional structural information and when compared to single-scale pixel-based CA model, it produces higher capacity to segment skin lesions with various sizes and contrast. We evaluated our method on two public skin lesion datasets and showed that it was more accurate and robust when compared to the state-of-the-art skin lesion segmentation methods.