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Automatic segmentation of dermoscopy images using saliency combined with adaptive thresholding based on wavelet transform

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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. The proposed method combines the enhanced fusion saliency with adaptive thresholding based on wavelet transform to get the lesion regions. Firstly, a fusion 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. We compare the proposed method with seven state-of-the-art approaches using a series of evaluation metrics on both PH2 and ISBI2016 datasets. The results demonstrate the effectiveness of the proposed method superior to the state-of-the-art approaches in accordance with quantitative results and visual effects.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61802328 and 61771415, and the Cernet Innovation Project under Grant NGII20170702.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China

    Kai Hu, Si Liu, Yuan Zhang, Chunhong Cao, Fen Xiao & Xieping Gao

  2. Postdoctoral Research Station for Mechanics, Xiangtan University, Xiangtan, 411105, China

    Kai Hu

  3. Department of Radiology, The First Hospital of Changsha, Changsha, 410005, China

    Wei Huang

Authors
  1. Kai Hu
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  2. Si Liu
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  3. Yuan Zhang
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  4. Chunhong Cao
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  5. Fen Xiao
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  6. Wei Huang
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  7. Xieping Gao
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Correspondence to Kai Hu.

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Hu, K., Liu, S., Zhang, Y. et al. Automatic segmentation of dermoscopy images using saliency combined with adaptive thresholding based on wavelet transform. Multimed Tools Appl 79, 14625–14642 (2020). https://doi.org/10.1007/s11042-019-7160-0

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