Abstract
Segmentation is the essential requirement in automated computer-aided diagnosis (CAD) of skin diseases. In this paper, we propose an unsupervised skin lesion segmentation method to challenge the difficulties existing in the dermoscopy images such as low contrast, border indistinct, and skin lesion is close to the boundary. Our method combines the enhanced fusion saliency with adaptive thresholding based on wavelet transform to get the lesion regions. Firstly, the saliency map increases the contract of the skin lesion and healthy skin, and then an adaptive thresholding method based on wavelet transform is used to obtain more accurate lesion regions. Experiments on dermoscopy images demonstrate the effectiveness of the proposed method over several state-of-the-art methods in terms of quantitative results and visual effects.
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This work was supported by the National Natural Science Foundation of China under Grants no. 61802328 and 61771415, and the Cernet Innovation Project under Grant no. NGII20170702.
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Hu, K. et al. (2020). A Skin Lesion Segmentation Method Based on Saliency and Adaptive Thresholding in Wavelet Domain. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_43
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DOI: https://doi.org/10.1007/978-3-030-04946-1_43
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