Abstract
This paper proposes an unsupervised skin lesion segmentation method for dermoscopy images by exploiting the contextual information of skin image at the superpixel level. In particular, a Laplacian sparse coding is presented to evaluate the probabilities of the skin image pixels to delineate lesion border. Moreover, a new rule-based smoothing strategy is proposed as the lesion segmentation refinement procedure. Finally, a multi-scale superpixel segmentation of the skin image is provided to handle size variation of the lesion in order to improve the accuracy of the detected border. Experiments conducted on two datasets show the superiority of our proposed method over several state-of-the-art skin segmentation methods.
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Notes
- 1.
m is the feature dimension.
- 2.
\(w_{ij}=exp\left( -\frac{\left\| y_{i},y_{j} \right\| }{\sigma ^{2}} \right) \quad s.t. \quad j\in NB\left( i \right) \).
- 3.
The parameter \(\beta \) is set to 0.2.
- 4.
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Bozorgtabar, B., Abedini, M., Garnavi, R. (2016). Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_31
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